Bibliographic record
Abstract
On Being a Doctor5 April 2011Lost in More Than Just TranslationBarbara M. Young, MD, CMBarbara M. Young, MD, CMFrom McGill University, Montreal, Quebec H3A 2T5, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-154-7-201104050-00010 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Having recently attended an excellent lecture on the evaluation of dementia, I was eager to test out my refreshed and updated skills on my next patient. “Failing to thrive. Patient difficult to evaluate,” the consult said. “Please rule out dementia.” Internet access is always very slow in Kuujjuaq, but, using the computer in the head nurse's office, I finally succeeded in downloading the cognitive assessment tool that had been suggested by the lecturer. I then retreated back to the little room designated for visiting specialists and sat down to read the patient's chart.I'd flown up to the subarctic community ... Author, Article, and Disclosure InformationAffiliations: From McGill University, Montreal, Quebec H3A 2T5, Canada.Corresponding Author: Barbara M. Young, MD, CM, Montreal General Hospital, Division of General Internal Medicine, Room B2-118, 1650 Cedar Avenue, Montreal, Quebec H3G 1A4, Canada. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics 5 April 2011Volume 154, Issue 7Page: 502KeywordsChartsComputersDementiaGamesHospital medicineInternetMemoryNursesQuestionnairesTwins ePublished: 5 April 2011 Issue Published: 5 April 2011 Copyright & PermissionsCopyright © 2011 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.390 | 0.351 |
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".