Bibliographic record
Abstract
Web Exclusives4 August 2020Annals Graphic Medicine - More Than MDFREEAnnie Zhu, BHSc and Arnav Agarwal, MDAnnie Zhu, BHScUniversity of Toronto, Toronto, Ontario, Canada (A.Z., A.A.)Search for more papers by this author and Arnav Agarwal, MDUniversity of Toronto, Toronto, Ontario, Canada (A.Z., A.A.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/G19-0090 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Download figure Download PowerPoint Comments0 CommentsSign In to Submit A Comment Author, Article, and Disclosure InformationAffiliations: University of Toronto, Toronto, Ontario, Canada (A.Z., A.A.)Disclosures: The authors have disclosed no conflicts of interest. The forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=G19-0090.Author/Illustrator Information: Annie Zhu is a medical student at the University of Toronto. She is interested in sequential art and exploring narratives in medicine. Her other illustrations and comics can be found at cazezhu.carbonmade.com. Dr. Arnav Agarwal is an internal medicine resident at the University of Toronto. He is passionate about clinical epidemiology research, health advocacy and allyship, and medical curriculum development and has a deep appreciation for art and narrative writing as media for reflection and advocacy. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics 4 August 2020Volume 173, Issue 3Page: W57-W58KeywordsClinical epidemiologyConflicts of interestDisclosureHealth care ePublished: 4 August 2020 Issue Published: 4 August 2020 Copyright & PermissionsCopyright © 2020 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.896 | 0.813 |
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".