Trends in Canadian hospital standardised mortality ratios and palliative care coding 2004–2010: a retrospective database analysis
Bibliographic record
Abstract
BACKGROUND: The hospital standardised mortality ratio (HSMR), anchored at an average score of 100, is a controversial macromeasure of hospital quality. The measure may be dependent on differences in patient coding, particularly since cases labelled as palliative are typically excluded. OBJECTIVE: To determine whether palliative coding in Canada has changed since the 2007 national introduction of publicly released HSMRs, and how such changes may have affected results. DESIGN: Retrospective database analysis. SETTING: Inpatients in Canadian hospitals from April 2004 to March 2010. PATIENTS: 12 593 329 hospital discharges recorded in the Canadian Institute for Health Information (CIHI) Discharge Abstract Database from April 2004 to March 2010. MEASUREMENTS: Crude mortality and palliative care coding rates. HSMRs calculated with the same methodology as CIHI. A derived hospital standardised palliative ratio (HSPR) adjusted to a baseline average of 100 in 2004-2005. Recalculated HSMRs that included palliative cases under varying scenarios. RESULTS: Crude mortality and palliative care coding rates have been increasing over time (p<0.001), in keeping with the nation's advancing overall morbidity. HSMRs in 2008-2010 were significantly lower than in 2004-2006 by 8.55 points (p<0.001). The corresponding HSPR rises dramatically between these two time periods by 48.83 points (p<0.001). Under various HSMR scenarios that included palliative cases, the HSMR would have at most decreased by 6.35 points, and may have even increased slightly. LIMITATIONS: Inability to calculate a definitively comparable HSMR that include palliative cases and to account for closely timed changes in national palliative care coding guidelines. CONCLUSIONS: Palliative coding rates in Canadian hospitals have increased dramatically since the public release of HSMR results. This change may have partially contributed to the observed national decline in HSMR.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".