Literature review for r&d program năm 2024

Question

A literature review is a type of academic writing that provides an overview of existing knowledge in a particular field of research.

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Discussions
How can I write literature review?
What is the R package for systematic reviews?
What is the difference between RRL and RRS?
How can I find literature review?

A good literature review summarises, analyses, evaluates and synthesises the relevant literature within a particular field of research. It illuminates how knowledge has evolved within the field, highlighting what has already been done, what is generally accepted, what is emerging and what is the current state of thinking on the topic. Additionally, literature reviews identify the gaps in the current knowledge - that is, uninvestigated or under-researched areas.

Whether the literature review is short or extended, similar structural and linguistic features apply.

You would like to do a systematic search of the scientific literature on a given topic. But a wild caveat appears - your familiarity (or lack there of) with the topic will bias your search, while your field lacks in standardized terminology and is fragmented into multiple nomenclature clusters.

Grames et al. (2019) devised a method to address this (you can read the article here). A gentle introduction to both R and

# Single query
term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])'  
pmid_list <- easyPubMed::get_pubmed_ids(term)
# as.integer(as.character(pmid_list$Count))    # get count of articles returned by query
# pmid_list$IdList                             # get PubMed IDs returned by query
# httr::BROWSE(
#   paste("http://www.ncbi.nlm.nih.gov/pubmed/", 
#     pmid_list$IdList[[1]], sep = ""
#   )
# )  # open in browser the abstract of first record in data frame 
pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list)
pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, 
                                           included_authors = "first", 
                                           getKeywords = TRUE, max_chars = 500)

9 package that trivializes the analyses can be found on Library Carpentry.

Simply put we can cast a wide net using a naïve search, retrieve relevant information from data bases (e.g., titles, keywords, abstracts) and analyse this interlinked text data to derive a systematic search strategy. I view it as way to bootstrap knowledge.

Representation of the litsearchr workflow (Grames et al., 2019) adapted by me for automated data base querying

We will use the following packages:

See results as.data.frame(lapply(head(pm_df[c("pmid", "doi", "jabbrv", "keywords", "abstract")]), substr, start = 1, stop = 30)) 0 that simplifies the use of the PubMed API to query and extract article data.
Single query term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])' pmid_list <- easyPubMed::get_pubmed_ids(term) as.integer(as.character(pmid_list$Count)) # get count of articles returned by query pmid_list$IdList # get PubMed IDs returned by query httr::BROWSE( paste("http://www.ncbi.nlm.nih.gov/pubmed/", pmid_list$IdList[[1]], sep = "" ) ) # open in browser the abstract of first record in data frame pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list) pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, included_authors = "first", getKeywords = TRUE, max_chars = 500) 9 for automated approach to identifying search terms for systematic reviews using keyword co-occurrence networks.
See results as.data.frame(lapply(head(pm_df[c("pmid", "doi", "jabbrv", "keywords", "abstract")]), substr, start = 1, stop = 30)) 2 for the Stopwords ISO Dataset which is the most comprehensive collection of stopwords for multiple languages.
See results as.data.frame(lapply(head(pm_df[c("pmid", "doi", "jabbrv", "keywords", "abstract")]), substr, start = 1, stop = 30))
3 for network analyses (this package is already a dependence of
Single query term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])' pmid_list <- easyPubMed::get_pubmed_ids(term) as.integer(as.character(pmid_list$Count)) # get count of articles returned by query pmid_list$IdList # get PubMed IDs returned by query httr::BROWSE( paste("http://www.ncbi.nlm.nih.gov/pubmed/", pmid_list$IdList[[1]], sep = "" ) ) # open in browser the abstract of first record in data frame pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list) pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, included_authors = "first", getKeywords = TRUE, max_chars = 500)
9 but there are still many useful functions that are not wrapped by
Single query term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])' pmid_list <- easyPubMed::get_pubmed_ids(term) as.integer(as.character(pmid_list$Count)) # get count of articles returned by query pmid_list$IdList # get PubMed IDs returned by query httr::BROWSE( paste("http://www.ncbi.nlm.nih.gov/pubmed/", pmid_list$IdList[[1]], sep = "" ) ) # open in browser the abstract of first record in data frame pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list) pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, included_authors = "first", getKeywords = TRUE, max_chars = 500) 9 functions).
See results as.data.frame(lapply(head(pm_df[c("pmid", "doi", "jabbrv", "keywords", "abstract")]), substr, start = 1, stop = 30))
6,
See results as.data.frame(lapply(head(pm_df[c("pmid", "doi", "jabbrv", "keywords", "abstract")]), substr, start = 1, stop = 30))
7, and
See results as.data.frame(lapply(head(pm_df[c("pmid", "doi", "jabbrv", "keywords", "abstract")]), substr, start = 1, stop = 30)) 8 for plotting.

1. Load or install packages

# litsearchr isn't yet on CRAN, need to install from github
if (require(litsearchr)) remotes::install_github("elizagrames/litsearchr", ref = "main")
# Packages to load/install
packages <- c(
  "easyPubMed",
  "litsearchr", "stopwords", "igraph", 
  "ggplot2", "ggraph", "ggrepel"
)
# Install packages not yet installed
installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
  install.packages(packages[!installed_packages])
}
# Load packages
lapply(packages, library, character.only = TRUE)

2. Query PubMed for relevant literature

2.1. Single query

This is to demonstrate the basic workflow for a single query. The

# See results
as.data.frame(lapply(head(pm_df[c("pmid", "doi", "jabbrv", "keywords", "abstract")]), substr, start = 1, stop = 30))

9 uses boolean operators to define a query for articles that contain “psychotherapy” and “PTSD” only from year 2020.

The

# See results
as.data.frame(lapply(head(pm_df[c("pmid", "doi", "jabbrv", "keywords", "abstract")]), substr, start = 1, stop = 30))

0 steps are quite simple: (1) retrieve PubMed article ids for the corresponding query using

##       pmid                         doi                        jabbrv
## 1 34629839 10.1016/j.cbpra.2020.09.002              Cogn Behav Pract
## 2 34471458  10.1007/s40653-020-00327-9        J Child Adolesc Trauma
## 3 33990240  10.1016/j.beth.2020.08.005                    Behav Ther
## 4 33739764                                                  Rev Prat
## 5 33552844  10.1007/s40501-020-00222-y Curr Treat Options Psychiatry
## 6 33520399                                       Innov Clin Neurosci
##                         keywords                       abstract
## 1 PTSD; cognitive processing the Of the many vulnerable groups 
## 2 Adolescents; Children; Evidenc Central American youth are at 
## 3 Cognitive Behavioral Therapy;  Fidelity monitoring is a criti
## 4 Fibromyalgia; Stress Disorders Fibromyalgia and post-traumati
## 5 Comorbidity; Pharmacotherapy;  Sleep disturbances, insomnia a
## 6 EMDR; eye movement desensitiza DEPARTMENT EDITORS Julie P. Ge

1, (2) fetch the actually data corresponding to the retrieved ids, and (3) extract and restructure relevant information.

# Single query
term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])'  
pmid_list <- easyPubMed::get_pubmed_ids(term)
# as.integer(as.character(pmid_list$Count))    # get count of articles returned by query
# pmid_list$IdList                             # get PubMed IDs returned by query
# httr::BROWSE(
#   paste("http://www.ncbi.nlm.nih.gov/pubmed/", 
#     pmid_list$IdList[[1]], sep = ""
#   )
# )  # open in browser the abstract of first record in data frame 
pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list)
pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, 
                                           included_authors = "first", 
                                           getKeywords = TRUE, max_chars = 500)

Check the first 10 entries:

# See results
as.data.frame(lapply(head(pm_df[c("pmid", "doi", "jabbrv", "keywords", "abstract")]), substr, start = 1, stop = 30))

##       pmid                         doi                        jabbrv
## 1 34629839 10.1016/j.cbpra.2020.09.002              Cogn Behav Pract
## 2 34471458  10.1007/s40653-020-00327-9        J Child Adolesc Trauma
## 3 33990240  10.1016/j.beth.2020.08.005                    Behav Ther
## 4 33739764                                                  Rev Prat
## 5 33552844  10.1007/s40501-020-00222-y Curr Treat Options Psychiatry
## 6 33520399                                       Innov Clin Neurosci
##                         keywords                       abstract
## 1 PTSD; cognitive processing the Of the many vulnerable groups 
## 2 Adolescents; Children; Evidenc Central American youth are at 
## 3 Cognitive Behavioral Therapy;  Fidelity monitoring is a criti
## 4 Fibromyalgia; Stress Disorders Fibromyalgia and post-traumati
## 5 Comorbidity; Pharmacotherapy;  Sleep disturbances, insomnia a
## 6 EMDR; eye movement desensitiza DEPARTMENT EDITORS Julie P. Ge

2.2. Multiple queries

We will search in Title/Abstract for the presence three terms (“psychotherapy” / “psychological intervention”, “trial” / “randomized”, “PTSD”) and constrain the search to years 2022 and 2023, respectively. The following outlines the way to use

##       pmid                         doi                        jabbrv
## 1 34629839 10.1016/j.cbpra.2020.09.002              Cogn Behav Pract
## 2 34471458  10.1007/s40653-020-00327-9        J Child Adolesc Trauma
## 3 33990240  10.1016/j.beth.2020.08.005                    Behav Ther
## 4 33739764                                                  Rev Prat
## 5 33552844  10.1007/s40501-020-00222-y Curr Treat Options Psychiatry
## 6 33520399                                       Innov Clin Neurosci
##                         keywords                       abstract
## 1 PTSD; cognitive processing the Of the many vulnerable groups 
## 2 Adolescents; Children; Evidenc Central American youth are at 
## 3 Cognitive Behavioral Therapy;  Fidelity monitoring is a criti
## 4 Fibromyalgia; Stress Disorders Fibromyalgia and post-traumati
## 5 Comorbidity; Pharmacotherapy;  Sleep disturbances, insomnia a
## 6 EMDR; eye movement desensitiza DEPARTMENT EDITORS Julie P. Ge

2 to generate combinations of the terms that will subsequently be used for multiple queries.

# Multiple queries
years <- c(2022:2023)
years <- sprintf('(("%d/01/01"[Date - Publication] : "%d/12/31"[Date - Publication]))', years, years)
term1 <- c('psychotherapy', 'psychological intervention') |> paste0('[Title/Abstract]')
term2 <- c('trial', 'randomized') |> paste0('[Title/Abstract]')
term3 <- c('PTSD') |> paste0('[Title/Abstract]')
search_terms <- expand.grid("year" = years, "term1" = term1, "term2" = term2, "term3" = term3)
search_terms$final <- 
  paste0(search_terms$year, ' AND ', 
    paste0('(', 
      apply(search_terms[, grep("term.", colnames(search_terms))], 1, paste, collapse = " AND ", sep = " "),
      ')'
    )
  )
pmid_list <- lapply(search_terms$final, easyPubMed::get_pubmed_ids)
# lapply(pmid_list, "[", "Count")    # get count per query
# lapply(pmid_list, "[", "IdList")   # get PubMed IDs per query
pm_xml <- lapply(pmid_list, easyPubMed::fetch_pubmed_data)
pm_df_list <- 
  lapply(pm_xml, easyPubMed::table_articles_byAuth, 
    included_authors = "first", getKeywords = TRUE, max_chars = 500
  )  # get only first author so that each study has only one row; multiple authors => long format data frame
pm_df_list <- Map(cbind, pm_df_list, 
    "query" = search_terms$final, 
    "terms" = apply(search_terms[, grep("term.", colnames(search_terms))], 1, paste, collapse = " AND ", sep = " ")
  ) # add the query strings that produced the results  
pm_df <- do.call(rbind, pm_df_list)  
pm_df <- pm_df[!duplicated(pm_df), ]  # exclude duplicates

Inspect terms:

search_terms

##                                                                      year
## 1 (("2022/01/01"[Date - Publication] : "2022/12/31"[Date - Publication]))
## 2 (("2023/01/01"[Date - Publication] : "2023/12/31"[Date - Publication]))
## 3 (("2022/01/01"[Date - Publication] : "2022/12/31"[Date - Publication]))
## 4 (("2023/01/01"[Date - Publication] : "2023/12/31"[Date - Publication]))
## 5 (("2022/01/01"[Date - Publication] : "2022/12/31"[Date - Publication]))
## 6 (("2023/01/01"[Date - Publication] : "2023/12/31"[Date - Publication]))
## 7 (("2022/01/01"[Date - Publication] : "2022/12/31"[Date - Publication]))
## 8 (("2023/01/01"[Date - Publication] : "2023/12/31"[Date - Publication]))
##                                        term1                      term2
## 1              psychotherapy[Title/Abstract]      trial[Title/Abstract]
## 2              psychotherapy[Title/Abstract]      trial[Title/Abstract]
## 3 psychological intervention[Title/Abstract]      trial[Title/Abstract]
## 4 psychological intervention[Title/Abstract]      trial[Title/Abstract]
## 5              psychotherapy[Title/Abstract] randomized[Title/Abstract]
## 6              psychotherapy[Title/Abstract] randomized[Title/Abstract]
## 7 psychological intervention[Title/Abstract] randomized[Title/Abstract]
## 8 psychological intervention[Title/Abstract] randomized[Title/Abstract]
##                  term3
## 1 PTSD[Title/Abstract]
## 2 PTSD[Title/Abstract]
## 3 PTSD[Title/Abstract]
## 4 PTSD[Title/Abstract]
## 5 PTSD[Title/Abstract]
## 6 PTSD[Title/Abstract]
## 7 PTSD[Title/Abstract]
## 8 PTSD[Title/Abstract]
##                                                                                                                                                                              final
## 1                   (("2022/01/01"[Date - Publication] : "2022/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND trial[Title/Abstract] AND PTSD[Title/Abstract])
## 2                   (("2023/01/01"[Date - Publication] : "2023/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND trial[Title/Abstract] AND PTSD[Title/Abstract])
## 3      (("2022/01/01"[Date - Publication] : "2022/12/31"[Date - Publication])) AND (psychological intervention[Title/Abstract] AND trial[Title/Abstract] AND PTSD[Title/Abstract])
## 4      (("2023/01/01"[Date - Publication] : "2023/12/31"[Date - Publication])) AND (psychological intervention[Title/Abstract] AND trial[Title/Abstract] AND PTSD[Title/Abstract])
## 5              (("2022/01/01"[Date - Publication] : "2022/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND randomized[Title/Abstract] AND PTSD[Title/Abstract])
## 6              (("2023/01/01"[Date - Publication] : "2023/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND randomized[Title/Abstract] AND PTSD[Title/Abstract])
## 7 (("2022/01/01"[Date - Publication] : "2022/12/31"[Date - Publication])) AND (psychological intervention[Title/Abstract] AND randomized[Title/Abstract] AND PTSD[Title/Abstract])
## 8 (("2023/01/01"[Date - Publication] : "2023/12/31"[Date - Publication])) AND (psychological intervention[Title/Abstract] AND randomized[Title/Abstract] AND PTSD[Title/Abstract])

Inspect literature trends with respect to our query:

pm_df$date <- as.Date(apply(pm_df[, c("year", "month", "day")], 1, paste0, collapse = "-"))
pm_df$ym <- format(pm_df$date, "%Y-%m")
pm_df$term <- gsub("\\[Title/Abstract\\]", "", pm_df$terms)
pm_df |>
  dplyr::count(term, ym) |>
  dplyr::mutate(date = as.Date(paste0(ym, "-01"))) |>
  ggplot(aes(x = date, y = n)) +
  geom_col() +
  facet_wrap(term ~ .) +
  scale_x_date(date_breaks = "1 year", date_minor_breaks = "2 months", date_labels = "%Y-%m") +
  xlab("Year") +
  ylab("Count") + 
  ggtitle("2022/2023: Psychotherapy trials for PTSD") +
  theme_bw()

Within

##       pmid                         doi                        jabbrv
## 1 34629839 10.1016/j.cbpra.2020.09.002              Cogn Behav Pract
## 2 34471458  10.1007/s40653-020-00327-9        J Child Adolesc Trauma
## 3 33990240  10.1016/j.beth.2020.08.005                    Behav Ther
## 4 33739764                                                  Rev Prat
## 5 33552844  10.1007/s40501-020-00222-y Curr Treat Options Psychiatry
## 6 33520399                                       Innov Clin Neurosci
##                         keywords                       abstract
## 1 PTSD; cognitive processing the Of the many vulnerable groups 
## 2 Adolescents; Children; Evidenc Central American youth are at 
## 3 Cognitive Behavioral Therapy;  Fidelity monitoring is a criti
## 4 Fibromyalgia; Stress Disorders Fibromyalgia and post-traumati
## 5 Comorbidity; Pharmacotherapy;  Sleep disturbances, insomnia a
## 6 EMDR; eye movement desensitiza DEPARTMENT EDITORS Julie P. Ge

3 use

##       pmid                         doi                        jabbrv
## 1 34629839 10.1016/j.cbpra.2020.09.002              Cogn Behav Pract
## 2 34471458  10.1007/s40653-020-00327-9        J Child Adolesc Trauma
## 3 33990240  10.1016/j.beth.2020.08.005                    Behav Ther
## 4 33739764                                                  Rev Prat
## 5 33552844  10.1007/s40501-020-00222-y Curr Treat Options Psychiatry
## 6 33520399                                       Innov Clin Neurosci
##                         keywords                       abstract
## 1 PTSD; cognitive processing the Of the many vulnerable groups 
## 2 Adolescents; Children; Evidenc Central American youth are at 
## 3 Cognitive Behavioral Therapy;  Fidelity monitoring is a criti
## 4 Fibromyalgia; Stress Disorders Fibromyalgia and post-traumati
## 5 Comorbidity; Pharmacotherapy;  Sleep disturbances, insomnia a
## 6 EMDR; eye movement desensitiza DEPARTMENT EDITORS Julie P. Ge

4 if keywords are provided. For extracting interesting words out of titles use the Rapid Automatic Keyword Extraction (RAKE) algorithm, coupled with a good stop word collection.

Here we will do both. My heuristic is when in doubt, pool results together.

# Extract terms from title
pm_terms_title <- litsearchr::extract_terms(text = pm_df[,"title"], 
                                            method = "fakerake", min_freq = 3, min_n = 2,
                                            stopwords = stopwords::data_stopwords_stopwordsiso$en)
# Extract terms from keywords
pm_terms_keywords <- litsearchr::extract_terms(keywords = trimws(unlist(strsplit(pm_df[,"keywords"], ";"))), 
                                               method = "tagged", min_freq = 3, min_n = 1, max_n = 5)
# Pool the extracted terms together
pm_terms <- c(pm_terms_title, pm_terms_keywords)
pm_terms <- pm_terms[!duplicated(pm_terms)]

4. Create Co-Occurrence Network

We will consider the title and abstract of each article to represent the article’s “content” and we will consider a term to have appeared in the article if it appears in either the title or abstract. Based on this we will create the document-feature matrix, where the “documents” are our articles (title and abstract) and the “features” are the search terms. The Co-Occurrence Network is computed using this document-feature matrix.

# Create Co-Occurrence Network
pm_docs <- paste(pm_df[, "title"], pm_df[, "abstract"]) # we will consider title and abstract of each article to represent the article's "content"
pm_dfm <- litsearchr::create_dfm(elements = pm_docs, features = pm_terms) # document-feature matrix
pm_coocnet <- litsearchr::create_network(pm_dfm, min_studies = 3)
ggraph(pm_coocnet, layout = "stress") +
  coord_fixed() +
  expand_limits(x = c(-3, 3)) +
  geom_edge_link(aes(alpha = weight)) +
  geom_node_point(shape = "circle filled", fill = "white") +
  geom_node_text(aes(label = name), hjust = "outward", check_overlap = TRUE) +
  guides(edge_alpha = "none") +
  theme_void()

5. Prune the Network based on node strength

5.1 Compute node strength

Node strength in a network is calculated by summing up the weights of all edges connected to the respective node.Thus, node strength investigates how strongly it is directly connected to other nodes in the network.

# Single query
term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])'  
pmid_list <- easyPubMed::get_pubmed_ids(term)
# as.integer(as.character(pmid_list$Count))    # get count of articles returned by query
# pmid_list$IdList                             # get PubMed IDs returned by query
# httr::BROWSE(
#   paste("http://www.ncbi.nlm.nih.gov/pubmed/", 
#     pmid_list$IdList[[1]], sep = ""
#   )
# )  # open in browser the abstract of first record in data frame 
pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list)
pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, 
                                           included_authors = "first", 
                                           getKeywords = TRUE, max_chars = 500)

0

5.1 Prune based on chosen criteria

We want to keep only those nodes that have high strength, but how will we decide how many to prune out?

##       pmid                         doi                        jabbrv
## 1 34629839 10.1016/j.cbpra.2020.09.002              Cogn Behav Pract
## 2 34471458  10.1007/s40653-020-00327-9        J Child Adolesc Trauma
## 3 33990240  10.1016/j.beth.2020.08.005                    Behav Ther
## 4 33739764                                                  Rev Prat
## 5 33552844  10.1007/s40501-020-00222-y Curr Treat Options Psychiatry
## 6 33520399                                       Innov Clin Neurosci
##                         keywords                       abstract
## 1 PTSD; cognitive processing the Of the many vulnerable groups 
## 2 Adolescents; Children; Evidenc Central American youth are at 
## 3 Cognitive Behavioral Therapy;  Fidelity monitoring is a criti
## 4 Fibromyalgia; Stress Disorders Fibromyalgia and post-traumati
## 5 Comorbidity; Pharmacotherapy;  Sleep disturbances, insomnia a
## 6 EMDR; eye movement desensitiza DEPARTMENT EDITORS Julie P. Ge

5 provides us with two ways to decide: cumulative cutoff and change points. The cumulative cutoff method simply retains a certain proportion of the total strength. The change points method uses

##       pmid                         doi                        jabbrv
## 1 34629839 10.1016/j.cbpra.2020.09.002              Cogn Behav Pract
## 2 34471458  10.1007/s40653-020-00327-9        J Child Adolesc Trauma
## 3 33990240  10.1016/j.beth.2020.08.005                    Behav Ther
## 4 33739764                                                  Rev Prat
## 5 33552844  10.1007/s40501-020-00222-y Curr Treat Options Psychiatry
## 6 33520399                                       Innov Clin Neurosci
##                         keywords                       abstract
## 1 PTSD; cognitive processing the Of the many vulnerable groups 
## 2 Adolescents; Children; Evidenc Central American youth are at 
## 3 Cognitive Behavioral Therapy;  Fidelity monitoring is a criti
## 4 Fibromyalgia; Stress Disorders Fibromyalgia and post-traumati
## 5 Comorbidity; Pharmacotherapy;  Sleep disturbances, insomnia a
## 6 EMDR; eye movement desensitiza DEPARTMENT EDITORS Julie P. Ge

6 under the hood to calculate optimal cutoff positions where the trend in strength shows sharp changes.

Again, we will use the heuristic when in doubt, pool results together, i.e. we will use the change point nearest the to the cumulative cutoff value we set.

# Single query
term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])'  
pmid_list <- easyPubMed::get_pubmed_ids(term)
# as.integer(as.character(pmid_list$Count))    # get count of articles returned by query
# pmid_list$IdList                             # get PubMed IDs returned by query
# httr::BROWSE(
#   paste("http://www.ncbi.nlm.nih.gov/pubmed/", 
#     pmid_list$IdList[[1]], sep = ""
#   )
# )  # open in browser the abstract of first record in data frame 
pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list)
pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, 
                                           included_authors = "first", 
                                           getKeywords = TRUE, max_chars = 500)

1

# Single query
term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])'  
pmid_list <- easyPubMed::get_pubmed_ids(term)
# as.integer(as.character(pmid_list$Count))    # get count of articles returned by query
# pmid_list$IdList                             # get PubMed IDs returned by query
# httr::BROWSE(
#   paste("http://www.ncbi.nlm.nih.gov/pubmed/", 
#     pmid_list$IdList[[1]], sep = ""
#   )
# )  # open in browser the abstract of first record in data frame 
pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list)
pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, 
                                           included_authors = "first", 
                                           getKeywords = TRUE, max_chars = 500)

2

Inspect selected terms:

# Single query
term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])'  
pmid_list <- easyPubMed::get_pubmed_ids(term)
# as.integer(as.character(pmid_list$Count))    # get count of articles returned by query
# pmid_list$IdList                             # get PubMed IDs returned by query
# httr::BROWSE(
#   paste("http://www.ncbi.nlm.nih.gov/pubmed/", 
#     pmid_list$IdList[[1]], sep = ""
#   )
# )  # open in browser the abstract of first record in data frame 
pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list)
pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, 
                                           included_authors = "first", 
                                           getKeywords = TRUE, max_chars = 500)

3

# Single query
term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])'  
pmid_list <- easyPubMed::get_pubmed_ids(term)
# as.integer(as.character(pmid_list$Count))    # get count of articles returned by query
# pmid_list$IdList                             # get PubMed IDs returned by query
# httr::BROWSE(
#   paste("http://www.ncbi.nlm.nih.gov/pubmed/", 
#     pmid_list$IdList[[1]], sep = ""
#   )
# )  # open in browser the abstract of first record in data frame 
pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list)
pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, 
                                           included_authors = "first", 
                                           getKeywords = TRUE, max_chars = 500)

4

We see that some term groupings are obvious: type of study (design), type of intervention, disorder/symptoms, and population. We are not interested in a particular population, so we will exclude terms like “child”, “adult”, “aged”, “veterans”. We will also exclude terms that are good candidates for these groups but are too wide for our scope: “mental health” is too wide for disorder/symptoms, while “psychological” and “treatment” are too wide for type of intervention. Afterwards we will manually define groupings for the rest of the terms.

6. Manual grouping into clusters

# Single query
term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])'  
pmid_list <- easyPubMed::get_pubmed_ids(term)
# as.integer(as.character(pmid_list$Count))    # get count of articles returned by query
# pmid_list$IdList                             # get PubMed IDs returned by query
# httr::BROWSE(
#   paste("http://www.ncbi.nlm.nih.gov/pubmed/", 
#     pmid_list$IdList[[1]], sep = ""
#   )
# )  # open in browser the abstract of first record in data frame 
pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list)
pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, 
                                           included_authors = "first", 
                                           getKeywords = TRUE, max_chars = 500)

5

7. Automatically write the search string

# Single query
term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])'  
pmid_list <- easyPubMed::get_pubmed_ids(term)
# as.integer(as.character(pmid_list$Count))    # get count of articles returned by query
# pmid_list$IdList                             # get PubMed IDs returned by query
# httr::BROWSE(
#   paste("http://www.ncbi.nlm.nih.gov/pubmed/", 
#     pmid_list$IdList[[1]], sep = ""
#   )
# )  # open in browser the abstract of first record in data frame 
pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list)
pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, 
                                           included_authors = "first", 
                                           getKeywords = TRUE, max_chars = 500)

6

# Single query
term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])'  
pmid_list <- easyPubMed::get_pubmed_ids(term)
# as.integer(as.character(pmid_list$Count))    # get count of articles returned by query
# pmid_list$IdList                             # get PubMed IDs returned by query
# httr::BROWSE(
#   paste("http://www.ncbi.nlm.nih.gov/pubmed/", 
#     pmid_list$IdList[[1]], sep = ""
#   )
# )  # open in browser the abstract of first record in data frame 
pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list)
pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, 
                                           included_authors = "first", 
                                           getKeywords = TRUE, max_chars = 500)

7

# Single query
term <- '(("2020/01/01"[Date - Publication] : "2020/12/31"[Date - Publication])) AND (psychotherapy[Title/Abstract] AND PTSD[Title/Abstract])'  
pmid_list <- easyPubMed::get_pubmed_ids(term)
# as.integer(as.character(pmid_list$Count))    # get count of articles returned by query
# pmid_list$IdList                             # get PubMed IDs returned by query
# httr::BROWSE(
#   paste("http://www.ncbi.nlm.nih.gov/pubmed/", 
#     pmid_list$IdList[[1]], sep = ""
#   )
# )  # open in browser the abstract of first record in data frame 
pm_xml <- easyPubMed::fetch_pubmed_data(pmid_list)
pm_df <- easyPubMed::table_articles_byAuth(pubmed_data = pm_xml, 
                                           included_authors = "first", 
                                           getKeywords = TRUE, max_chars = 500)

8

Discussions

Only the last two steps, pertaining to term exclusion and term grouping, need the careful decisions of a human researcher. The automatic workflow, on it’s own, found some important terms that I would have surely omitted. I am especially pleased about “exposure therapy” which is a prevalent class of psychotherapy interventions but is called a “therapy”, making it harder to distinguish from other terms that end in “therapy” but are not psychotherapies.

References

Grames, E. M., Stillman, A. N., Tingley, M. W., & Elphick, C. S. (2019). An automated approach to identifying search terms for systematic reviews using keyword co‐occurrence networks. Methods in Ecology and Evolution, 10(10), 1645-1654.

How can I write literature review?

How do I write a literature review?.

Step 1: Define your research scope. What is the specific research question that your literature review helps to define? ... .

Step 2: Identify the literature. Start by searching broadly. ... .

Step 3: Critically analyze the literature. ... .

Step 4: Categorize your resources..

What is the R package for systematic reviews?

robvis is an R package that allows users to quickly visualise risk-of-bias assessments performed as part of a systematic review.

What is the difference between RRL and RRS?

Related literature are often from journalist or any officials whereas the opinions and facts presented matters and can affect the masses' opinion and thinking. Related Studies are from researchers or from official public offices, and thesis from different universities and libraries.

How can I find literature review?

To find the literature review section in a scholarly article:.

Scholarly publications (especially peer reviewed articles) will always include at least a brief literature review in their introduction and discussion sections. ... .

Theses and dissertations should include a detailed literature review..

đánh giá Review Meta-analysis in R R Discovery Google Scholar R litsearchr R Research Bibliometric analysis PubMed

Literature review for r&d program năm 2024

See results

Single query

as.integer(as.character(pmid_list$Count)) # get count of articles returned by query

pmid_list$IdList # get PubMed IDs returned by query

httr::BROWSE(

paste("http://www.ncbi.nlm.nih.gov/pubmed/",

pmid_list$IdList[[1]], sep = ""

)

) # open in browser the abstract of first record in data frame

See results

See results

Single query

as.integer(as.character(pmid_list$Count)) # get count of articles returned by query

pmid_list$IdList # get PubMed IDs returned by query

httr::BROWSE(

paste("http://www.ncbi.nlm.nih.gov/pubmed/",

pmid_list$IdList[[1]], sep = ""

)

) # open in browser the abstract of first record in data frame

Single query

as.integer(as.character(pmid_list$Count)) # get count of articles returned by query

pmid_list$IdList # get PubMed IDs returned by query

httr::BROWSE(

paste("http://www.ncbi.nlm.nih.gov/pubmed/",

pmid_list$IdList[[1]], sep = ""

)

) # open in browser the abstract of first record in data frame

See results

See results

See results

1. Load or install packages

2. Query PubMed for relevant literature

2.1. Single query

2.2. Multiple queries

4. Create Co-Occurrence Network

5. Prune the Network based on node strength

5.1 Compute node strength

5.1 Prune based on chosen criteria

6. Manual grouping into clusters

7. Automatically write the search string

Discussions

References

How can I write literature review?

What is the R package for systematic reviews?

What is the difference between RRL and RRS?

How can I find literature review?

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