All Hands on Deck: Learning to “Un‐specialize” in the COVID‐19 Pandemic
Bibliographic record
Abstract
Journal of Hospital MedicineVolume 15, Issue 5 p. 314-315 Perspectives in Hospital Medicine All Hands on Deck: Learning to "Un-specialize" in the COVID-19 Pandemic Peter Cram MD, MBA, Corresponding Author Peter Cram MD, MBA [email protected] Division of General Internal Medicine and Geriatrics, Sinai Health System and University Health Network, Toronto, Canada Faculty of Medicine, University of Toronto, Toronto, CanadaCorresponding Author: Peter Cram, MD, MBA; Email: [email protected]; Twitter: @pmcram.Search for more papers by this authorMel L Anderson MD, Mel L Anderson MD Primary and Specialty Care Service Line, Minneapolis VA Health Care System, Minneapolis, MinnesotaSearch for more papers by this authorErin E Shaughnessy MD, MSHCM, Erin E Shaughnessy MD, MSHCM Division of Hospital Medicine, Phoenix Children's Hospital, Phoenix, Arizona Department of Pediatrics, University of Arizona College of Medicine-Phoenix, Phoenix, ArizonaSearch for more papers by this author Peter Cram MD, MBA, Corresponding Author Peter Cram MD, MBA [email protected] Division of General Internal Medicine and Geriatrics, Sinai Health System and University Health Network, Toronto, Canada Faculty of Medicine, University of Toronto, Toronto, CanadaCorresponding Author: Peter Cram, MD, MBA; Email: [email protected]; Twitter: @pmcram.Search for more papers by this authorMel L Anderson MD, Mel L Anderson MD Primary and Specialty Care Service Line, Minneapolis VA Health Care System, Minneapolis, MinnesotaSearch for more papers by this authorErin E Shaughnessy MD, MSHCM, Erin E Shaughnessy MD, MSHCM Division of Hospital Medicine, Phoenix Children's Hospital, Phoenix, Arizona Department of Pediatrics, University of Arizona College of Medicine-Phoenix, Phoenix, ArizonaSearch for more papers by this author First published: 07 April 2020 https://doi.org/10.12788/jhm.3426Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Graphical Abstract References 1Smith A. An Inquiry into the Nature and Causes of the Wealth of Nations. Chicago, Illinois: University of Chicago Press; 1976. Google Scholar 2Cram P, Ettinger WH, Jr. Generalists or specialists–who does it better? Physician Exec. 1998; 24(1): 40–45. CASPubMedGoogle Scholar 3 Accreditation Council for Graduate Medical Education. ACGME Response to Pandemic Crisis. https://acgme.org/COVID-19. Accessed April 1, 2020. Google Scholar 4 The Joint Commission. Emergency Management—Meeting FPPE and OPPE Requirements During the COVID-19 Emergency. https://www.jointcommission.org/standards/standard-faqs/hospital-and-hospital-clinics/medical-staff-ms/000002291/. Accessed April 1, 2020. Google Scholar 5Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. PLoS One. 2020; 15(3):e0231236. https://doi.org/10.1371/journal.pone.0231236.eCollection2020. 10.1371/journal.pone.0231236 CASPubMedWeb of Science®Google Scholar 6Ioannidis JPA. Coronavirus disease 2019: the harms of exaggerated information and non-evidence-based measures. Eur J Clin Invest. 2020;e13222. https://doi.org/10.1111/eci.13222. Google Scholar 7Antommaria M. Conflicting duties and reciprocal obligations during a pandemic. J Hosp Med. 2020; 15(5): 284–286. https://doi.org/10.12788/jhm.3425. 10.12788/jhm.3425 PubMedWeb of Science®Google Scholar Citing Literature Volume15, Issue5May 2020Pages 314-315 ReferencesRelatedInformation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".