Bibliographic record
Abstract
Web Exclusives8 June 2021Annals Graphic Medicine - Last Human Contact in COVID-19FREEPooja Gandhi, MSpPathSt and Arnav Agarwal, MDPooja Gandhi, MSpPathStToronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada (P.G.)Search for more papers by this author and Arnav Agarwal, MDUniversity of Toronto, Toronto, Ontario, Canada (A.A.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/G20-0090 SectionsAboutVisual AbstractPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Download figure Download PowerPoint Author, Article, and Disclosure InformationAuthors: Pooja Gandhi, MSpPathSt; Arnav Agarwal, MDAffiliations: Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada (P.G.)University of Toronto, Toronto, Ontario, Canada (A.A.)Disclosures: Authors have reported no disclosures of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=G20-0090.Author/Illustrator Information: Pooja Gandhi, MSpPathSt, is a registered speech-language pathologist working at University Health Network (Toronto, Ontario) and Joseph Brant Hospital (Burlington, Ontario), and is a PhD candidate at University of Toronto (Toronto, Ontario) (e-mail, pooja.gandhi@uhn.ca). Arnav Agarwal, MD, is a resident physician in internal medicine at University of Toronto (Toronto, Ontario), and an incoming clinical fellow in general internal medicine at McMaster University (Hamilton, Ontario) (e-mail, arnav.agarwal@mail.utoronto.ca).This article was published at Annals.org on 8 June 2021. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics June 2021Volume 174, Issue 6 Page: W58-W59 ePublished: 8 June 2021 Issue Published: June 2021 Copyright & PermissionsCopyright © 2021 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.965 | 0.930 |
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".