MétaCan
Menu
Back to cohort
Record W4410775469 · doi:10.1136/bmj-2024-083866

Core GRADE 6: presenting the evidence in summary of findings tables

2025· article· en· W4410775469 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMJ · 2025
Typearticle
Languageen
FieldComputer Science
TopicMachine Learning in Healthcare
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsCore (optical fiber)Computer scienceInformation retrievalData scienceTelecommunications

Abstract

fetched live from OpenAlex

This sixth article in a seven part series presents the Core GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach to summary of findings tables. These tables provide essential information about the effects of interventions on patient important outcomes, including relative and absolute effects, certainty of evidence, and a plain language summary. For binary outcomes calculating absolute effects requires applying relative risk estimates to baseline risks from studies representative of the target population. For groups of patients with very different baseline risks, summary of findings tables include separate rows with different estimates of absolute effects. For continuous outcomes, challenges arise when individual studies use different instruments to measure patient reported outcomes. Facilitating interpretation then requires providing details about units of measurement and minimally important differences.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.196

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.110
GPT teacher head0.397
Teacher spread0.287 · 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