Bibliographic record
Abstract
Web ExclusivesJune 2022Annals Graphic Medicine - Dr. Mom: Medical ConferenceFREEGrace E. Farris, MDGrace E. Farris, MDUniversity of Texas, Austin, Texas (G.E.F.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/G22-0022 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Download figure Download PowerPoint Comments0 CommentsSign In to Submit A Comment Karima KhamisaUniversity of Ottawa25 May 2022 Can relate! Felt this way at a conference recently! Author, Article, and Disclosure InformationAffiliations: University of Texas, Austin, Texas (G.E.F.)Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=G22-0020.Author/Illustrator Information: Grace E. Farris, MD, is an assistant professor of medicine at Dell Medical School at the University of Texas in Austin, Texas. You can view her work at her website: www.farrisgrace.com.About Dr. Mom: In Dr. Mom, physician and cartoonist Grace Farris examines work–life balance while mothering and doctoring.This article was published at Annals.org on 24 May 2022. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics June 2022Volume 175, Issue 6Page: W65-W66 ePublished: 24 May 2022 Issue Published: June 2022 Copyright & PermissionsCopyright © 2022 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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.882 | 0.669 |
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".