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Record W2749237168 · doi:10.1503/cmaj.733261

Beware selection bias

2017· letter· en· W2749237168 on OpenAlexaffvenue
Jon Barrett, Arthur Zaltz, Michael Geary, Mathew Sermer, John Kingdom

Bibliographic record

VenueCanadian Medical Association Journal · 2017
Typeletter
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsSelection (genetic algorithm)PopulationFront lineComputer scienceSelection biasData scienceMedicineArtificial intelligencePolitical sciencePathologyEnvironmental healthLaw

Abstract

fetched live from OpenAlex

As experienced front-line obstetricians with leadership responsibilities, we have concerns with the conclusions and implications of the article by Muraca and colleagues on operative delivery in the second stage of labour.[1][1] Although an important strength is the size of this population-based

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)0.144
metaresearch head score (Gemma)0.529
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.856
Threshold uncertainty score0.762

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1440.529
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0020.003
Scholarly communication0.0040.006
Open science0.0020.003
Research integrity0.0080.007
Insufficient payload (model declined to judge)0.0220.005

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 head0.038
GPT teacher head0.326
Teacher spread0.288 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainMethods
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

Citations5
Published2017
Admission routes2
Has abstractyes

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