Development and validation of a predictive model for all-cause hospital readmissions in Winnipeg, Canada
Bibliographic record
Abstract
OBJECTIVE: A number of predictive models have been developed to identify patients at risk of hospital readmission. Most of these have focused on readmission within 30 days of discharge. We used population-based health administrative data to develop a predictive model for hospital readmission within 12 months of discharge in Winnipeg, Canada. METHODS: This was a retrospective cohort study with derivation and validation data sets. Multivariable logistic regression analyses were performed and factors significantly associated with readmission were selected to construct a risk scoring tool. RESULTS: Several variables were identified that predicted readmission (i.e. older age, male, at least one hospital admission in the previous two years, an emergent (index) hospital admission, Charlson comorbidity score >0 and length of stay). Discrimination power was acceptable (C statistic =0.701). At a median risk score threshold, the sensitivity, specificity, positive and negative predictive values were 45.5%, 79%, 68.8% and 58.6%. CONCLUSIONS: This predictive model demonstrated that hospital readmission within 12 months of discharge can be reasonably well predicted based on administrative data. It will help health care providers target interventions to prevent unnecessary hospital readmissions.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".