MétaCan
Menu
Back to cohort
Record W1911361640 · doi:10.1186/s12916-015-0465-6

A scoping review of rapid review methods

2015· review· en· W1911361640 on OpenAlexafffund
Andrea C. Tricco, Jesmin Antony, Wasifa Zarin, Lisa Strifler, Marco Ghassemi, John D. Ivory, Laure Perrier, Brian Hutton, David Moher, Sharon E. Straus

Bibliographic record

VenueBMC Medicine · 2015
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsCanada Research ChairsOttawa HospitalInstitute for Work & HealthPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
FundersNational Institute on Deafness and Other Communication DisordersCanadian Institutes of Health Research
KeywordsMedicineSystematic reviewMEDLINECochrane LibraryLimitingCritical appraisalInclusion (mineral)Meta-analysisAlternative medicinePathologyPsychology

Abstract

fetched live from OpenAlex

BACKGROUND: Rapid reviews are a form of knowledge synthesis in which components of the systematic review process are simplified or omitted to produce information in a timely manner. Although numerous centers are conducting rapid reviews internationally, few studies have examined the methodological characteristics of rapid reviews. We aimed to examine articles, books, and reports that evaluated, compared, used or described rapid reviews or methods through a scoping review. METHODS: MEDLINE, EMBASE, the Cochrane Library, internet websites of rapid review producers, and reference lists were searched to identify articles for inclusion. Two reviewers independently screened literature search results and abstracted data from included studies. Descriptive analysis was conducted. RESULTS: We included 100 articles plus one companion report that were published between 1997 and 2013. The studies were categorized as 84 application papers, seven development papers, six impact papers, and four comparison papers (one was included in two categories). The rapid reviews were conducted between 1 and 12 months, predominantly in Europe (58 %) and North America (20 %). The included studies failed to report 6 % to 73 % of the specific systematic review steps examined. Fifty unique rapid review methods were identified; 16 methods occurred more than once. Streamlined methods that were used in the 82 rapid reviews included limiting the literature search to published literature (24 %) or one database (2 %), limiting inclusion criteria by date (68 %) or language (49 %), having one person screen and another verify or screen excluded studies (6 %), having one person abstract data and another verify (23 %), not conducting risk of bias/quality appraisal (7 %) or having only one reviewer conduct the quality appraisal (7 %), and presenting results as a narrative summary (78 %). Four case studies were identified that compared the results of rapid reviews to systematic reviews. Three studies found that the conclusions between rapid reviews and systematic reviews were congruent. CONCLUSIONS: Numerous rapid review approaches were identified and few were used consistently in the literature. Poor quality of reporting was observed. A prospective study comparing the results from rapid reviews to those obtained through systematic reviews is warranted.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
gptMetaresearch
Domain: Methods · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.197
metaresearch head score (Gemma)0.444
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.803
Threshold uncertainty score0.990

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1970.444
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0080.008
Bibliometrics0.0520.040
Science and technology studies0.0040.004
Scholarly communication0.0120.014
Open science0.0060.008
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0170.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.967
GPT teacher head0.740
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Study designSystematic review
DomainMethods
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1,156
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueBMC MedicineSame topicMeta-analysis and systematic reviewsCategoryMetaresearchFrench-language works237,207