CIHI's Hospital Standardized Mortality Ratio: Friend or Foe?
Bibliographic record
Abstract
Hospital standardized mortality ratios (HSMRs) for acute care hospitals across Canada (excluding Quebec) were released in November 2007 by the Canadian Institute for Health Information. Since the release, some hospitals have undertaken in-depth analyses of their HSMRs to make sense of their results. In this issue of Healthcare Papers, Penfold et al. describe their experiences with the measure, pointing out shortcomings with using such a highly aggregated measure of hospital performance. We echo their concerns with the HSMR and highlight the caveats to interpreting this measure. However, we also point out that, despite its limitations, the HSMR stimulated the authors to probe, on behalf of their institution, factors that may have influenced mortality rates. This probing underlines the merit of HSMR reporting and the types of insights and knowledge that are likely to be gained if other institutions undertake similar evaluations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.009 |
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; both teacher heads agree on what is shown here.
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".