Bibliographic record
Abstract
Web Exclusives19 September 2017Annals Story Slam - Karl's StoryFREENatalie Wong, MDNatalie Wong, MDFrom University of Toronto, Toronto, Ontario, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/W17-0056 SectionsAboutVisual Abstract ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Video. Annals Story Slam - Karl's Story "Karl's Story," by Dr. Natalie Wong (Duration 06:50)Building on the popular Annals feature “On Being a Doctor,” storytellers share stories about the experience of doctoring on video.For more videos from and information on Annals Story Slam, visit go.annals.org/StorySlam. Comments0 CommentsSign In to Submit A Comment Author, Article, and Disclosure InformationAffiliations: From University of Toronto, Toronto, Ontario, Canada.Current Author Address: Natalie Wong, MD, University of Toronto, Toronto, Ontario, Canada; e-mail, [email protected]ca. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics 19 September 2017Volume 167, Issue 6Page: SS1 ePublished: 19 September 2017 Issue Published: 19 September 2017 Copyright & PermissionsCopyright © 2017 by American College of Physicians. All Rights Reserved.Loading ...
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.439 | 0.212 |
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".