Bibliographic record
Abstract
Web Exclusives3 January 2017Annals Graphic Medicine - Breach of ConfidentialityFREECaroline Shooner, MDCaroline Shooner, MDSearch for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/G16-0019 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Download figure Download PowerPoint Comments0 CommentsSign In to Submit A Comment Author, Article, and Disclosure InformationAffiliations: Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=G16-0019.Author Information: Dr. Shooner is a general practitioner on the remote archipelago of Haida Gwaii, in western Canada. Her cartoons (www.theboondocs.org) explore the comic side of small town medicine.Corresponding Author: Caroline Shooner, MD, PO Box 669, 3110, 2nd Avenue, Queen Charlotte, British Columbia V0T 1S0, Canada; e-mail, [email protected]com.Author Contributions: Conception and design: C. Shooner.Final approval of the article: C. Shooner. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics 3 January 2017Volume 166, Issue 1Page: W1-W2KeywordsDisclosureHealth care ePublished: 3 January 2017 Issue Published: 3 January 2017 Copyright & PermissionsCopyright © 2017 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 | 0.012 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.280 | 0.085 |
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".