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Record W2399566326 · doi:10.1503/cmaj.151470

Guiding the reporting of studies that use routinely collected health data

2016· article· en· W2399566326 on OpenAlexaffvenue
Hude Quan, Tyler Williamson

Bibliographic record

VenueCanadian Medical Association Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicChronic Disease Management Strategies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

See also page [555][1] and [www.cmaj.ca/lookup/doi/10.1503/cmaj.160410][2], [www.cmaj.ca/lookup/doi/10.1503/cmaj.150653][3] and CMAJ Open article [www.cmajopen.ca/content/4/2/E132][4] In the last decade, there has been an explosion of digital information, and health information is no exception.

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)0.878
metaresearch head score (Gemma)0.927
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.122
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8780.927
Meta-epidemiology (narrow)0.0050.008
Meta-epidemiology (broad)0.0110.010
Bibliometrics0.0460.049
Science and technology studies0.0070.020
Scholarly communication0.0400.028
Open science0.0230.024
Research integrity0.0280.021
Insufficient payload (model declined to judge)0.0110.015

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 head0.278
GPT teacher head0.402
Teacher spread0.124 · 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
DomainReporting
GenreMethods

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

Citations9
Published2016
Admission routes2
Has abstractyes

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