Predicting drug overdose and death after “before medically advised” hospital discharge
Bibliographic record
Abstract
BACKGROUND: "Before medically advised" (BMA) hospital discharge is associated with higher risks for subsequent death and drug overdose. We sought to develop prediction models to estimate the absolute risk of death from any cause and illicit drug overdose after BMA discharge for a given patient. METHODS: We used retrospective population-based administrative health data of a 20% random sample of all British Columbia residents to derive and internally validate regression models to estimate the risks of death (model A) and illicit drug overdose (model B) within 30 days after BMA discharge. Model A included all nonelective, nonobstetrical adult hospitalizations ending in BMA discharge between 2015 and 2019. Model B included only hospitalizations from model A for patients with evidence of prior substance use. We fitted prediction models using logistic regression and validated models using bootstrap-based optimism correction. Candidate predictors included sociodemographic and clinical characteristics available to clinicians during BMA discharge. RESULTS: Of 6440 hospital admissions included in model A, 102 (1.6%) were associated with the death of a patient within 30 days of BMA discharge. Predictors for death included a Charlson Comorbidity Index of 2 or higher, cancer, and heart disease. Model A exhibited good discrimination (C-statistic = 0.78) and excellent calibration. Of 4466 hospital admissions included in model B, 233 (5.2%) were associated with a patient subsequently overdosing within 30 days of BMA discharge. Predictors for drug overdose included homelessness, receipt of social income assistance, opioid use disorder, non-alcohol substance use disorder, overdose in the past year, and discharge from a surgical service. Model B exhibited good discrimination (C-statistic = 0.79) and excellent calibration. INTERPRETATION: Risk prediction models may help clinicians and hospitals identify patients who might benefit from intensive support to prevent death or illict drug overdose after BMA discharge.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".