Bibliographic record
Abstract
Letters5 July 2005Clinical Experience and Quality of Health CareGeoffrey R. Norman, PhD and Kevin W. Eva, PhDGeoffrey R. Norman, PhDFrom McMaster University, Hamilton, Ontario L8S 4L8, Canada.Search for more papers by this author and Kevin W. Eva, PhDFrom McMaster University, Hamilton, Ontario L8S 4L8, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-143-1-200507050-00018 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:The article by Choudhry and colleagues (1) showing a consistent negative relationship between experience and performance is controversial. Although few will be surprised that recent graduates do better on knowledge tests or are more likely to adhere to practice guidelines, the relationship to patient outcomes, particularly mortality, is much more difficult to dismiss. In particular, the single unequivocal study, by Norcini and colleagues (2), observed “a 0.5% increase in mortality for every year [since graduation].” Taken at face value, this is a terrifying statistic. If a recent graduate has a 10% in-hospital mortality rate, then someone who ...References1. Choudhry NK, Fletcher RH, Soumerai SB. Systematic review: the relationship between clinical experience and quality of health care. Ann Intern Med. 2005;142:260-73. [PMID: 15710959] LinkGoogle Scholar2. Norcini JJ, Kimball HR, Lipner RS. Certification and specialization: do they matter in the outcome of acute myocardial infarction? Acad Med. 2000;75:1193-8. [PMID: 11112721] CrossrefMedlineGoogle Scholar3. Norcini JJ, Lipner RS, Kimball HR. Certifying examination performance and patient outcomes following acute myocardial infarction. Med Educ. 2002;36:853-9. [PMID: 12354248] CrossrefMedlineGoogle Scholar4. Hartz AJ, Kuhn EM, Pulido J. Prestige of training programs and experience of bypass surgeons as factors in adjusted patient mortality rates. Med Care. 1999;37:93-103. [PMID: 10413397] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From McMaster University, Hamilton, Ontario L8S 4L8, Canada.Disclosures: None disclosed. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoSystematic Review: The Relationship between Clinical Experience and Quality of Health Care Niteesh K. Choudhry , Robert H. Fletcher , and Stephen B. Soumerai Clinical Experience and Quality of Health Care Elizabeth W. Loder Clinical Experience and Quality of Health Care Niteesh K. Choudhry , Robert H. Fletcher , and Stephen B. Soumerai Clinical Experience and Quality of Health CareClinical Experience and Quality of Health Care Martin A. Samuels and Allan H. Ropper Clinical Experience and Quality of Health Care Roy M. Poses and Joseph A. Diaz Clinical Experience and Quality of Health Care Ronald S. Szabo Metrics Cited ByAccepting Diagnostic Suggestions by Residents: A Potential Cause of Diagnostic Error in MedicineThe American College of Chest Physicians Evidence-Based Educational Guidelines for Continuing Medical Education InterventionsClinical Diagnostic Reasoning 5 July 2005Volume 143, Issue 1Page: 85-86KeywordsClimbingConflicts of interestDeath ratesGraduate medical educationHealth care qualityStatistical data ePublished: 5 July 2005 Issue Published: 5 July 2005 CopyrightCopyright © 2005 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 | 0.011 | 0.138 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.002 |
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".