Bibliographic record
Abstract
Web ExclusivesApril 2023Annals Graphic Medicine - Break the Chain, Stop the CycleFREEZainab Haji, BDS and Hina Inam, FCPSZainab Haji, BDSAga Khan University Hospital, Karachi, Pakistan (Z.H., H.I.)Search for more papers by this author and Hina Inam, FCPSAga Khan University Hospital, Karachi, Pakistan (Z.H., H.I.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/G22-0041 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Download figure Download PowerPoint Download figure Download PowerPoint Download figure Download PowerPoint Download figure Download PowerPoint Download figure Download PowerPoint Download figure Download PowerPoint Download figure Download PowerPoint Download figure Download PowerPoint Comments0 CommentsSign In to Submit A Comment Author, Article, and Disclosure InformationAuthors: Zainab Haji, BDS; Hina Inam, FCPSAffiliations: Aga Khan University Hospital, Karachi, Pakistan (Z.H., H.I.)Disclosures: Authors have reported no disclosures of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=G22-0041.Author/Illustrator Information: Zainab Haji, BDS, is a dentist, trained for FCPS at The Aga Khan University Hospital, Karachi, Pakistan (e-mail, [email protected]com). Hina Inam, FCPS Cardiothoracic Surgery, is currently a Fellow at Saint Boniface Hospital, Winnipeg, Manitoba (e-mail, [email protected]com).This article was published at Annals.org on 11 April 2023. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics April 2023Volume 176, Issue 4 ePublished: 11 April 2023 Issue Published: April 2023 Copyright & PermissionsCopyright © 2023 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.679 | 0.592 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".