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Record W4392186129 · doi:10.1136/bmj-2023-076518

Correcting the scientific record on abortion and mental health outcomes

2024· article· en· W4392186129 on OpenAlexaff
Julia H. Littell, Kathryn M. Abel, M. Antonia Biggs, Robert W. Blum, Diana Greene Foster, Lisa B. Haddad, Brenda Major, Trine Munk‐Olsen, Chelsea B. Polis, Gail Erlick Robinson, Corinne H. Rocca, Nancy Felipe Russo, Julia R. Steinberg, Donna E. Stewart, Nada L. Stotland, Ushma D. Upadhyay, Jenneke van Ditzhuijzen

Bibliographic record

VenueBMJ · 2024
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Contraception
Canadian institutionsToronto General HospitalUniversity of Toronto
Fundersnot available
KeywordsAbortionPublic healthMental healthAlternative medicineClinical PracticeMedicineComputer sciencePsychiatryFamily medicineNursingPregnancyPathologyBiology

Abstract

fetched live from OpenAlex

Julia Littell and colleagues argue that better adherence to ethical standards for correction or retraction of unreliable publications is essential to avoid harmful effects on public policy, clinical practice, and public health

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)0.254
metaresearch head score (Gemma)0.760
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.746
Threshold uncertainty score0.920

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2540.760
Meta-epidemiology (narrow)0.0010.003
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0200.015
Science and technology studies0.0060.015
Scholarly communication0.0200.009
Open science0.0070.008
Research integrity0.0270.028
Insufficient payload (model declined to judge)0.0100.007

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 head0.039
GPT teacher head0.395
Teacher spread0.355 · 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 designNot applicable
DomainEvaluation
GenreCommentary

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

Citations13
Published2024
Admission routes1
Has abstractyes

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