How Can We Know Whether Short Term Trends in a Hospital's HSMR are Significant?
Bibliographic record
Abstract
The Hospital Standardized Mortality Ratio (HSMR) has been chosen by CIHI as its primary mortality measure. The indirect standardization used in the calculation of HSMR does not allow for valid comparison between hospitals but it does invite the assessment of quarterly trends in hospital mortality. However, statistical methods for assessing HSMR trends are not well-developed. In 2007 one large hospital in our health authority had four consecutive quarters of apparently increasing HSMR. As a result, we needed to assess the significance of this trend which, if it were to continue into the next quarter, would lead to an HSMR that significantly exceeded 100. We explored four methods to assess statistical significance of time trends in HSMR data: the WINPEPI "Describe" module, the CUSUM representation of Observed-Expected differences, the Variable Life Adjusted Display (VLAD) plots with CUSUM overlays, and the Change Point Analysis using Monte Carlo simulation.
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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 | 0.027 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".