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 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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.043 | 0.011 |
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".