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Keeping Meta-analyses Fresh

2022· article· en· W4292939868 on OpenAlexaff
Jesse A. Berlin, Gordon D. Rubenfeld, Roisin E. O’Cearbhaill, Amy S. Shah, Stephan D. Fihn

Bibliographic record

VenueJAMA Network Open · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsSunnybrook Health Science CentreUniversity of Toronto
FundersSeagenGlaxoSmithKlineAtara BiotherapeuticsRegeneron PharmaceuticalsGenentechCelgeneAstraZeneca
KeywordsComputer science

Abstract

fetched live from OpenAlex

Systematic reviews and meta-analyses are valuable tools for understanding existing-and shaping future-medical research. The concepts of systematic reviews and meta-analysis were introduced more than 40 years ago and adopted into mainstream medical research shortly thereafter. When well performed, including the choice of a relevant topic, systematic reviews and meta-analyses can provide useful insights into disease diagnosis, epidemiology, treatment effects (including benefits and harms), and aspects of study design or populations that might be associated with (and possibly influence) treatment effectiveness or other outcomes, including adverse events. These effects may become apparent in a meta-analysis when not readily evident from individual small studies. Nearly half a century later, these objectives remain central, although additional applications and more rigorous methods have been developed. wever, as noted in a commentary by Berlin et al, 4 there is great variability in the quality of published meta-analyses, particularly with regard to whether they were conducted in a transparent, reproducible, and valid manner. In this vein, there are numerous existing guidance documents relating to the conduct or reporting of systematic reviews and meta-analyses, eg, the Cochrane Handbook and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. other issue is the massive increase in meta-analyses and systematic reviews published during the past 3 decades, many of which are likely redundant or misleading. This trend presents a challenge for the editors of JAMA Network Open in terms of the volume of systematic reviews and meta-analyses, including network meta-analyses, that are routinely submitted. In 2021, there were 655 meta-analyses and 175 systematic reviews submitted, with 56 (9%) and 29 (17%) accepted, respectively. Although we have not established explicit standards for determining whether a submitted systematic review or meta-analysis should be accepted, the editors of JAMA Network Open have implicitly developed criteria by which to judge these submissions. The issues extend beyond methodological rigor, which is a fundamental requirement, to include the crucial questions of what makes the meta-analysis important and interesting. To clarify our expectations, and in the process, improve the chance that a manuscript might be considered further, we ask that authors of meta-analyses and systematic reviews to address a series of key questions in their cover letter:

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)0.280
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.811
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2800.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0000.004
Science and technology studies0.0010.000
Scholarly communication0.0050.001
Open science0.0100.004
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.3460.004

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 head0.929
GPT teacher head0.607
Teacher spread0.321 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2022
Admission routes1
Has abstractyes

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