Understanding and Using the Hospital Standardized Mortality Ratio in Canada: Challenges and Opportunities
Bibliographic record
Abstract
In 2005, the Canadian Institute for Health Information (CIHI) began a methodological journey to develop a Canadian version of the hospital standardized mortality ratio (HSMR). For two years, CIHI worked with hospitals, regional authorities and measurement experts to define the most appropriate methodology given Canadian datasets and systems of care. In November 2007, we made the findings publicly available for regional health authorities and larger facilities. In their lead article, Penfold et al. discuss their views regarding some methodological issues and potential limitations of the HSMR to monitor quality of care and, in particular, as a patient safety indicator. Here we respond to their specific concerns and maintain that the HSMR remains an important tool in the arsenal of information hospitals can use to focus the discussion of patient safety/quality improvement, monitor the provision of care over time and identify opportunities for improvement.
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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 | 0.045 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.014 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 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".