Predictors of Death From Physical Illness or Accidental/Intentional Causes Among Patients With Substance-Related Disorders
Bibliographic record
Abstract
OBJECTIVE: This study identified patient clinical and sociodemographic characteristics, and, more originally, service use patterns as predictors of death from physical illness or accidental/intentional causes. METHODS: A cohort of 19,015 patients with substance-related disorders (SRD) from 14 addiction treatment centers was investigated using Quebec (Canada) health administrative databases. Death was studied over a 3-year period (April 1, 2013, to March 31, 2016), and most predictors from 4 years to 12 months prior to the time of death, using multinomial logistic regression. RESULTS: Frequent emergency department (ED) use strongly predicted both causes of death, suggesting that outpatient care responded inadequately to patient needs. Only receipt of specialized SRD and psychiatric care significantly decreased the risk of death from physical illness, with trends toward significance for accidental/intentional death. Hospitalization, greater material deprivation and having SRD-chronic physical illnesses or alcohol-related disorders most strongly predicted risk of death from physical illness. Sociodemographic characteristics, mainly social deprivation, were more likely to predict accidental/intentional death. CONCLUSIONS: Outpatient services could be improved by increasing outreach and motivational interventions and, for ED and hospital units, better screening, brief intervention, and referral to treatment, particularly for men and socially deprived patients at high risk of accidental/intentional death. Patients with more severe health conditions, notably older or materially deprived men at higher risk of death from physical illness, could benefit from programs like assertive community treatment or intensive case management that respond well to diverse and continuous patient needs. Collaborative care between SRD and health services could also be improved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".