Bibliographic record
Abstract
OBJECTIVES: Acute myocardial infarction (AMI) 'report cards' are being developed using administrative databases in many jurisdictions, but little is known about their acceptance by and their usefulness to the medical community. The purpose of this study was to determine the impact of the publication of Cardiovascular Health and Services in Ontario: An ICES Atlas (Naylor CD, Slaughter P. (eds), 1999, Toronto: ICES), the first report featuring hospital-specific AMI performance measures to be published in Canada. DESIGN: We conducted a mail survey of physicians at Ontario hospitals to determine their views on the usefulness of various atlas performance measures for assessing and improving quality of care, the types of quality initiatives launched at their hospital in response to the atlas, and their views on the concept and limitations of reporting hospital-specific AMI mortality data. RESULTS: Respondents to the survey indicated that information on process of care measures such as post-infarction beta-blocker and angiotensin-converting enzyme (ACE) inhibitor use, and cardiac procedure waiting times were the most useful, and outcomes data (e.g. 30-day and 1-year risk-adjusted AMI mortality rates) the least useful of the multiple performance measures published in the atlas (P = 0.0385). Fifty-four percent of respondents reported launching one or more quality of care initiatives at their hospital in response to the atlas. The majority of respondents (65%) indicated that they support the public release of hospital-specific AMI mortality data, although many had concerns about potential miscoding in administrative databases and the adequacy of risk-adjustment methods. CONCLUSION: The publication of the first AMI report card in Canada stimulated quality of care initiatives at many Ontario hospitals. Inclusion of performance measures other than mortality in health care report cards may lead to greater acceptance and use by the medical community.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".