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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.1,2 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.3 However, 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.5,6 Another 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.7 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:• Why is a new meta-analysis or network meta-analysis needed?What do we not already know that the current analysis can address?A meta-analysis that addresses an egregious error in a prior analysis and makes this correction the focus of the report may be appropriate for JAMA Network Open, particularly if it relates to an important clinical question with public health implications.• What meta-analyses on this topic have been conducted during the last 5 years, and how do the studies included in the current submission compare with those included in prior meta-analyses and/or network meta-analyses?There must be a compelling rationale to publish this type of replicative research in JAMA Network Open.An incremental update of existing meta-analyses would receive a lower priority and probably is better suited to other venues for living and dynamic reviews.8• Does the meta-analysis address a secondary question (eg, using metaregression to evaluate factors that might modify treatment effectiveness, risk factors in a subgroup of special interest)?These often represent novel questions and are a higher priority for JAMA Network Open.• Is the systematic review or meta-analysis unique?The first meta-analysis on a topic, even if the results merely show that there is very little literature on a given topic, can be of some value and

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 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)0.292
metaresearch head score (Gemma)0.684
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.708
Threshold uncertainty score0.874

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2920.684
Meta-epidemiology (narrow)0.0050.007
Meta-epidemiology (broad)0.0150.014
Bibliometrics0.0200.016
Science and technology studies0.0030.013
Scholarly communication0.0260.043
Open science0.0110.012
Research integrity0.0170.059
Insufficient payload (model declined to judge)0.0160.009

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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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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