Bibliographic record
Abstract
BACKGROUND: A relative dearth of relevant data hampers efforts to demonstrate a link between educational and clinical quality and may preclude residency applicants from identifying programs with the best clinical outcomes. Existing clinical rankings could fill this gap if they are based on sound judgments about quality. METHOD: To explore the potential of the U.S. News & World Report "America's Best Hospitals" clinical rankings in measuring the quality of clinical and learning environments, the author systematically reviewed the U.S. and Canadian literature for 1975 through 2007 regarding quality indicators and teaching hospitals. Individual data elements of the rankings were examined to assess the extent to which they included accepted measures of clinical performance. RESULTS: A total of 187 articles met the inclusion criteria of addressing clinical quality criteria relevant to the rankings and quality assessment in teaching hospitals. Statistical examination of the data underlying the rankings and their relationship with measures of educational and clinical quality showed the rankings are largely based on institutional "prestige." Ranked clinical programs and institutions consistently outperform counterparts on available indices, suggesting that the data elements underlying the rankings may provide valid assessments about the quality of care in educational settings. CONCLUSIONS: Data elements in the rankings can be used to assess clinical and, to a lesser extent, educational quality, but the number of specialties and ranked institutions is too small to have a significant effect on widespread clinical or educational quality, unless ranked institutions serve as sites for the development, study, and dissemination of best practices.
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 | 0.047 | 0.384 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.022 | 0.038 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".