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Record W2808900852 · doi:10.1111/1471-0528.15275

Getting this right from the start will ensure findings from clinical trials can be used effectively

2018· article· en· W2808900852 on OpenAlexaboutno aff
Natalie Am Cooper

Bibliographic record

VenueBJOG An International Journal of Obstetrics & Gynaecology · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsCaesarean sectionClinical trialMedicineConfidence intervalOutcome (game theory)Psychological interventionIntensive care medicinePregnancyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

core outcome sets and how are they developed?

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)0.654
metaresearch head score (Gemma)0.835
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.346
Threshold uncertainty score0.427

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6540.835
Meta-epidemiology (narrow)0.0060.010
Meta-epidemiology (broad)0.0250.019
Bibliometrics0.0250.015
Science and technology studies0.0080.019
Scholarly communication0.0660.100
Open science0.0110.029
Research integrity0.0430.058
Insufficient payload (model declined to judge)0.0470.059

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 head0.222
GPT teacher head0.503
Teacher spread0.281 · 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
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

Citations2
Published2018
Admission routes1
Has abstractyes

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