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Record W4234676039 · doi:10.5195/jmla.2016.141

Increasing number of databases searched in systematic reviews and meta-analyses between 1994 and 2014

2017· article· en· W4234676039 on OpenAlexaff
Michael Thomas Lam, Mary McDiarmid

Bibliographic record

VenueJournal of the Medical Library Association JMLA · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsOntario Shores Centre for Mental Health SciencesWestern University
Fundersnot available
KeywordsMilestoneSystematic reviewDatabaseMEDLINEMeta-analysisCochrane LibraryBibliographic databaseMedicineLibrary scienceComputer scienceGeographyInternal medicinePolitical scienceCartography

Abstract

fetched live from OpenAlex

Objectives: The purpose of this study was to determine whether the number of bibliographic databases used to search the health sciences literature in individual systematic reviews (SRs) and meta-analyses (MAs) changed over a twenty-year period related to the official 1995 launch of the Cochrane Database of Systematic Reviews (CDSR).Methods: Ovid MEDLINE was searched using a modified version of a strategy developed by the Scottish Intercollegiate Guidelines Network to identify SRs and MAs. Records from 3 milestone years were searched: the year immediately preceding (1994) and 1 (2004) and 2 (2014) decades following the CDSR launch. Records were sorted with randomization software. Abstracts or full texts of the records were examined to identify database usage until 100 relevant records were identified from each of the 3 years.Results: The mean and median number of bibliographic databases searched in 1994, 2004, and 2014 were 1.62 and 1, 3.34 and 3, and 3.73 and 4, respectively. Studies that searched only 1 database decreased over the 3 milestone years (60% in 1994, 28% in 2004, and 10% in 2014).Conclusions: The number of bibliographic databases searched in individual SRs and MAs increased from 1994 to 2014.

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

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.225
metaresearch head score (Gemma)0.328
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2250.328
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.817
GPT teacher head0.588
Teacher spread0.229 · 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

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations7
Published2017
Admission routes1
Has abstractyes

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