Bibliographic record
Abstract
Letters20 January 2004Implications of Regional Differences in SpendingRudy Fernandes, BSc (Hon)Rudy Fernandes, BSc (Hon)From Mississauga, Ontario L4W 2G6, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-140-2-200401200-00021 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR:The recent articles by Fisher and colleagues (1, 2) highlighting the implications of regional variations in Medicare spending have added to our understanding of the importance of effective medical spending. The authors should be applauded for challenging the general assumption that additional spending on health services will necessarily provide important health benefits.It is interesting to note that these findings seem to complement a Canadian study by Zelder (3), which found that regions with higher overall government health spending per capita had no effect on reducing patient waiting times. However, the Zelder study did note that the ...References1. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in Medicare spending. Part 1: the content, quality, and accessibility of care. Ann Intern Med. 2003;138:273-87. [PMID: 12585825] LinkGoogle Scholar2. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in Medicare spending. Part 2: health outcomes and satisfaction with care. Ann Intern Med. 2003;138:288-98. [PMID: 12585826] LinkGoogle Scholar3. Zelder M. Fraser Forum, Canada, 2003. Google Scholar4. Michaud CM, Murray CJ, Bloom BR. Burden of disease—implications for future research. JAMA. 2001;285:535-9. [PMID: 11176854] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: From Mississauga, Ontario L4W 2G6, Canada. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoThe Implications of Regional Variations in Medicare Spending. Part 1: The Content, Quality, and Accessibility of Care Elliott S. Fisher , David E. Wennberg , Thrse A. Stukel , Daniel J. Gottlieb , F. L. Lucas , and Étoile L. Pinder The Implications of Regional Variations in Medicare Spending. Part 2: Health Outcomes and Satisfaction with Care Elliott S. Fisher , David E. Wennberg , Thrse A. Stukel , Daniel J. Gottlieb , F. L. Lucas , and Étoile L. Pinder Implications of Regional Differences in Spending George L. Weber Implications of Regional Differences in Spending Hyman Gaylis Implications of Regional Differences in Spending Franklin Gaylis Implications of Regional Differences in Spending Elliott S. Fisher , Dan Gottlieb , and David Wennberg Implications of Regional Differences in Spending Rudolph J. Mueller Implications of Regional Differences in Spending Barry Kisloff Implications of Regional Differences in Spending Howard A. Levin Metrics 20 January 2004Volume 140, Issue 2Page: 146-147KeywordsAge groupsDrugsForecastingHealth careMedical servicesMedicare ePublished: 20 January 2004 Issue Published: 20 January 2004 CopyrightCopyright © 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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 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".