How to Achieve a High-Performance Health Care System in the United States
Bibliographic record
Abstract
Letters6 May 2008How to Achieve a High-Performance Health Care System in the United StatesRichard L. Cruess, MD and Sylvia R. Cruess, MDRichard L. Cruess, MDFrom the Center for Medical Education, McGill University, Montréal, Québec H3A T25, Canada.Search for more papers by this author and Sylvia R. Cruess, MDFrom the Center for Medical Education, McGill University, Montréal, Québec H3A T25, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-148-9-200805060-00018 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:When comparing the health care system in the United States with the national health plans in other countries, we can learn many lessons about health outcomes and health economics. With no national health plan and an emphasis on market forces, the United States is unprepared for negotiations between government and physicians.In a national health program, individuals and organizations representing both physicians and patients sit around a negotiating table and hammer out the terms of medical practice. In the United Kingdom, which has a highly centralized system, representatives of the Department of Health—representing society—sit at the table ...Reference1. Stevens RA. Public roles for the medical profession in the United States: beyond theories of decline and fall. Milbank Q. 2001;79:327-53, III. [PMID: 11565160] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From the Center for Medical Education, McGill University, Montréal, Québec H3A T25, Canada.Disclosures: None disclosed. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoAchieving a High-Performance Health Care System with Universal Access: What the United States Can Learn from Other CountriesHow to Achieve a High-Performance Health Care System in the United States Thomas James III How to Achieve a High-Performance Health Care System in the United States Harvey S. Zarren How to Achieve a High-Performance Health Care System in the United States Jack A. Ginsburg , Robert B. Doherty , and J. Fred Ralston Jr. How to Achieve a High-Performance Health Care System in the United States S. Ward Casscells and MG Elder Granger How to Achieve a High-Performance Health Care System in the United States Arvind R. Cavale How to Achieve a High-Performance Health Care System in the United States Richard S. Leff Metrics 6 May 2008Volume 148, Issue 9Page: 710-711KeywordsAttentionConflicts of interestHealth careHealth economicsHealth insuranceSub-specialty care ePublished: 6 May 2008 Issue Published: 6 May 2008 CopyrightCopyright © 2008 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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.035 | 0.007 |
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".