Underlying Polysubstance Classes and Associated Sociodemographic Characteristics and Health Histories among People who Died from Substance-Related Acute Toxicity in Canada: A Latent Class Analysis
Bibliographic record
Abstract
Abstract The aim of this study was to examine underlying patterns of substances detected among accidental acute toxicity deaths in Canada and their associations with sociodemographic characteristics, location, and substance use and health history. Data abstracted from coroner and medical examiner files for all accidental acute toxicity deaths across Canada (2016 to 2017) were analyzed. Six classes emerged from a latent class analysis conducted to characterize detected substance classes: (1) cocaine and alcohol, (2) benzodiazepines and other pharmaceutical substances, (3) pharmaceutical opioids, (4) multiple pharmaceutical and non-pharmaceutical substances, (5) methamphetamine and fentanyl or analogues, and (6) fentanyl or analogues. Differences were identified between latent classes by sex, age, marital status, location of death, place of residence, and substance use and health history. Patterns of detected substances among deaths characterized in this study emphasize the complex nature of substance-related acute toxicity deaths across Canada and can inform future research and public health action.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".