Bibliographic record
Abstract
On Being a Patient18 November 2014LoveJean-Noel Vergnes, DDS, PhDJean-Noel Vergnes, DDS, PhDFrom Toulouse Dental Faculty, Toulouse, France, and Division of Oral Health and Society, McGill University Montreal, Quebec, CanadaSearch for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/M14-1076 Audio Reading - “Love” Audio. Michael A. LaCombe, MD, Annals Associate Editor, reads "Love," by J.N. Vergnes. Your browser does not support the audio element. Audio player progress bar Step backward in current audio track Play current audio trackPause current audio track Step forward in current audio track Mute current audio trackUnmute current audio track 00:00/ SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail At 86, I haven't got any teeth. Haven't for a long time. I wear false ones. With the special cream on the back every morning, they hold in well. It's more difficult for my wife, Angela, though. She had a stroke 3 years ago. We're the same age, but her health has gone downhill badly in the past few years. In fact, even before the stroke, we thought that Alzheimer's might be starting.I've become what they call her “informal carer.” After her stroke, I was completely lost. Everything had to be reorganized. It wasn't easy. I'd never have believed ... Author, Article, and Disclosure InformationAffiliations: From Toulouse Dental Faculty, Toulouse, France, and Division of Oral Health and Society, McGill University Montreal, Quebec, CanadaCorresponding Author: Jean-Noel Vergnes, DDS, PhD, Toulouse Dental Faculty, 3 Chemin des Maraîchers, 31062 Toulouse Cedex 9, France; e-mail, jn.[email protected]ca. PreviousarticleNextarticle Advertisement Audio Reading - “Love” Audio. Michael A. LaCombe, MD, Annals Associate Editor, reads "Love," by J.N. Vergnes. Your browser does not support the audio element. Audio player progress bar Step backward in current audio track Play current audio trackPause current audio track Step forward in current audio track Mute current audio trackUnmute current audio track 00:00/ FiguresReferencesRelatedDetails Metrics Cited byA Reflection Curriculum for Longitudinal Community-Based Clinical Experiences: Impact on Student Perceptions of the Safety NetWhat about narrative dentistry? 18 November 2014Volume 161, Issue 10Page: 758-759KeywordsCaregiversChildrenFoodMouthNursesNursing homesRunningStrokeTouchWheelchairs ePublished: 18 November 2014 Issue Published: 18 November 2014 Copyright & PermissionsCopyright © 2014 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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.246 | 0.131 |
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".