Bibliographic record
Abstract
Letters2 August 2011Music LessonsAyodele Odutayo, BHSc and Rashida Williams, HBScAyodele Odutayo, BHScFrom University of Toronto, Toronto, Ontario, Canada M5S 1A1.Search for more papers by this author and Rashida Williams, HBScFrom University of Toronto, Toronto, Ontario, Canada M5S 1A1.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-155-3-201108020-00027 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:Davidoff's article (1) immediately struck a chord with us and piqued our interest. As long-time musicians, we could not help but to reflect on how music was already making a difference in our careers as students.For the past 9 years, we have proudly participated in a local steel pan orchestra. Although many may recall steel pan music from a recent Caribbean vacation, playing the steel pan is a unique musical experience. The performance usually occurs without the direction of a conductor and without the aid of sheet music. This setting therefore adds a new twist to ...Reference1. Davidoff F. Music lessons: what musicians can teach doctors (and other health professionals). Ann Intern Med. 2011;154:426-9. [PMID: 21403078] LinkGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From University of Toronto, Toronto, Ontario, Canada M5S 1A1.Note: This letter is dedicated to the memory of Elton “Smokey” John, founder and leader of the Mississauga Academy of Steelband Music.Disclosures: None disclosed. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoMusic Lessons: What Musicians Can Teach Doctors (and Other Health Professionals) Frank Davidoff Music Lessons Justine V. Cohen Metrics 2 August 2011Volume 155, Issue 3Page: 207KeywordsConflicts of interestForecastingHealth careMemory ePublished: 2 August 2011 Issue Published: 2 August 2011 CopyrightCopyright © 2011 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.668 | 0.402 |
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".