Bibliographic record
Abstract
Letters3 February 2004Adverse Events following Discharge from the HospitalAlan J. Forster, MD, FRCPC, MSc and David W. Bates, MD, MScAlan J. Forster, MD, FRCPC, MScFrom Ottawa Health Research Institute and University of Ottawa, Ottawa, Ontario K1Y 4E9, Canada; and Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115.Search for more papers by this author and David W. Bates, MD, MScFrom Ottawa Health Research Institute and University of Ottawa, Ottawa, Ontario K1Y 4E9, Canada; and Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-140-3-200402030-00022 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:We apologize to Dr. Rifas, and other readers, who had the misimpression that we were suggesting that adverse events postdischarge were “simply due to stupid doctors.” We strongly agree that preventable adverse events occur primarily because of systems issues (1). Our intent was to try to shed light on the extent of a problem that we feel is the result of changes in the health system, specifically the increasing fragmentation and complexity of care. Certainly, the unnecessarily complicated rules that patients face regarding health insurance reimbursements, which Dr. Rifas and Drs. Leff and Boult rightly underscore, contribute to ...References1. Leape LL, Bates DW, Cullen DJ, Cooper J, Demonaco HJ, Gallivan T, et al . Systems analysis of adverse drug events. ADE Prevention Study Group. JAMA. 1995;274:35-43. [PMID: 7791256] CrossrefMedlineGoogle Scholar2. Bates DW, Leape LL, Cullen DJ, Laird N, Petersen LA, Teich JM, et al . Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA. 1998;280:1311-6. [PMID: 9794308] CrossrefMedlineGoogle Scholar3. Hayward RA, Hofer TP. Estimating hospital deaths due to medical errors: preventability is in the eye of the reviewer. JAMA. 2001;286:415-20. [PMID: 11466119] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: Alan J. Forster, MD, FRCPC, MSc; David W. Bates, MD, MScAffiliations: From Ottawa Health Research Institute and University of Ottawa, Ottawa, Ontario K1Y 4E9, Canada; and Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoThe Incidence and Severity of Adverse Events Affecting Patients after Discharge from the Hospital Alan J. Forster , Harvey J. Murff , Josh F. Peterson , Tejal K. Gandhi , and David W. Bates Adverse Events following Discharge from the Hospital Donald C. Rifas Adverse Events following Discharge from the Hospital Bruce Leff and Chad Boult Adverse Events following Discharge from the Hospital Matthew M. Medlock , Louis R. Cantilena Jr. , and Michael A. Riel Adverse Events following Discharge from the Hospital Rodney A. Hayward and Timothy P. Hofer Metrics 3 February 2004Volume 140, Issue 3Page: 232-233KeywordsAdverse eventsAttentionConfidence limitDecision makingHealth insuranceQuality improvement ePublished: 3 February 2004 Issue Published: 3 February 2004 Copyright & PermissionsCopyright © 2004 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 | 0.003 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".