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Record W4403295421 · doi:10.1016/j.ajogmf.2024.101481

Trustworthiness criteria for meta-analyses of randomized controlled studies: OBGYN journal guidelines

2024· editorial· en· W4403295421 on OpenAlexaff
Jason Abbott, Ganesh Acharya, Amir Aviram, Kurt T. Barnhart, Vincenzo Berghella, Catherine S. Bradley, Nancy C. Chescheir, Thomas D’Hooghe, Michael Geary, Janesh Gupta, Karl Oliver Kagan, Anthony Odibo, Aris T. Papageorghiou, Luis Sanchez‐Ramos, Donna A. Santillan, Elizabeth Stringer, Togas Tulandi, Susan C. Modesitt

Bibliographic record

VenueAmerican Journal of Obstetrics & Gynecology MFM · 2024
Typeeditorial
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsCanarieIntecsea (Canada)
Fundersnot available
KeywordsTrustworthinessRandomized controlled trialMeta-analysisPsychologyMedicineComputer scienceInternal medicineSocial psychology

Abstract

fetched live from OpenAlex

Editors of a number of OBGYN journals have formed an OBGYN Editors’ Integrity Group (OGEIG), with the aim of collaborating to improve trustworthiness in published papers.1 These Editors have already jointly agreed and published quality criteria for randomized controlled trials (RCTs), incorporating the guidance in their instructions to authors.2 The requirements for RCTs include:

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)0.425
metaresearch head score (Gemma)0.950
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.791
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.4250.950
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0960.057
Bibliometrics0.0070.006
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0060.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.699
GPT teacher head0.608
Teacher spread0.091 · 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
GenreEditorial

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
Published2024
Admission routes1
Has abstractyes

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