Graduate Medical Education and Patient Safety
Bibliographic record
Abstract
Letters1 May 2007Graduate Medical Education and Patient SafetyKaveh G. Shojania, MD, Kathlyn E. Fletcher, MD, MA, and Sanjay Saint, MD, MPHKaveh G. Shojania, MDFrom Ottawa Health Research Institute, Ottawa, K1Y 4E9 Ontario, Canada; Clement J. Zablocki Veterans Affairs Medical Center and Medical College of Wisconsin, Milwaukee, WI 53295; and University of Michigan Medical School, Ann Arbor, MI 48109.Search for more papers by this author, Kathlyn E. Fletcher, MD, MAFrom Ottawa Health Research Institute, Ottawa, K1Y 4E9 Ontario, Canada; Clement J. Zablocki Veterans Affairs Medical Center and Medical College of Wisconsin, Milwaukee, WI 53295; and University of Michigan Medical School, Ann Arbor, MI 48109.Search for more papers by this author, and Sanjay Saint, MD, MPHFrom Ottawa Health Research Institute, Ottawa, K1Y 4E9 Ontario, Canada; Clement J. Zablocki Veterans Affairs Medical Center and Medical College of Wisconsin, Milwaukee, WI 53295; and University of Michigan Medical School, Ann Arbor, MI 48109.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-146-9-200705010-00019 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE: We thank Dr. Fine for his kind remarks about the Quality Grand Rounds series. Our article highlighted the importance of clear communication and the ways in which failure to explain key aspects of the care plan contributed to several errors in the case. We focused on physician trainees because of space limitations. However, we agree that poor physician–nurse communication contributed to the mistaken insertion of a feeding tube instead of a nasogastric tube, and failings in this area are an important source of medical errors.We agree with Dr. Griner that the use of simulation promises to improve ...References1. Hillman K, Chen J, Cretikos M, Bellomo R, Brown D, Doig G, et al. Introduction of the medical emergency team (MET) system: a cluster-randomised controlled trial. Lancet. 2005;365:2091-7. [PMID: 15964445] CrossrefMedlineGoogle Scholar2. Winters BD, Pham J, Pronovost PJ. Rapid response teams—walk, don't run. JAMA. 2006;296:1645-7. [PMID: 17018807] CrossrefMedlineGoogle Scholar3. Goldacre MJ, Roberts SE. Hospital admission for acute pancreatitis in an English population, 1963-98: database study of incidence and mortality. BMJ. 2004;328:1466-9. [PMID: 15205290] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From Ottawa Health Research Institute, Ottawa, K1Y 4E9 Ontario, Canada; Clement J. Zablocki Veterans Affairs Medical Center and Medical College of Wisconsin, Milwaukee, WI 53295; and University of Michigan Medical School, Ann Arbor, MI 48109.Disclosures: None disclosed. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoGraduate Medical Education and Patient Safety: A Busy—and Occasionally Hazardous—Intersection Kaveh G. Shojania , Kathlyn E. Fletcher , and Sanjay Saint Graduate Medical Education and Patient Safety Matthew N. Fine Graduate Medical Education and Patient Safety Paul F. Griner Graduate Medical Education and Patient Safety Stephen R. Workman Metrics 1 May 2007Volume 146, Issue 9Page: 686KeywordsConflicts of interestElderlyGraduate medical educationMotivationNursesPancreatitisSafety ePublished: 1 May 2007 Issue Published: 1 May 2007 CopyrightCopyright © 2007 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 | 0.005 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.341 | 0.072 |
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".