Derivation and validation of a diagnostic score based on case-mix groups to predict 30-day death or urgent readmission.
Bibliographic record
Abstract
BACKGROUND: Between 5% and 10% of patients die or are urgently readmitted within 30 days of discharge from hospital. Readmission risk indexes have either excluded acute diagnoses or modelled them as multiple distinct variables. In this study, we derived and validated a score summarizing the influence of acute hospital diagnoses and procedures on death or urgent readmission within 30 days. METHODS: From population-based hospital abstracts in Ontario, we randomly sampled 200 000 discharges between April 2003 and March 2009 and determined who had been readmitted urgently or died within 30 days of discharge. We used generalized estimating equation modelling, with a sample of 100 000 patients, to measure the adjusted association of various case-mix groups (CMGs-homogenous groups of acute care inpatients with similar clinical and resource-utilization characteristics) with 30-day death or urgent readmission. This final model was transformed into a scoring system that was validated in the remaining 100 000 patients. RESULTS: Patients in the derivation set belonged to 1 of 506 CMGs and had a 6.8% risk of 30-day death or urgent readmission. Forty-seven CMG codes (more than half of which were directly related to chronic diseases) were independently associated with this outcome, which led to a CMG score that ranged from -6 to 7 points. The CMG score was significantly associated with 30-day death or urgent readmission (unadjusted odds ratio for a 1-point increase in CMG score 1.52, 95% confidence interval [CI] 1.49-1.56). Alone, the CMG score was only moderately discriminative (C statistic 0.650, 95% CI 0.644-0.656). However, when the CMG score was added to a validated risk index for death or readmission, the C statistic increased to 0.759 (95% CI 0.753-0.765). The CMG score was well calibrated for 30-day death or readmission. INTERPRETATION: In this study, we developed a scoring system for acute hospital diagnoses and procedures that could be used as part of a risk-adjustment methodology for analyses of postdischarge outcomes.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".