Association between illicit drug overdose and encephalopathy in British Columbia, Canada: A cross-sectional analysis
Bibliographic record
Abstract
Abstract Background In the context of the drug poisoning (overdose) crisis in British Columbia (BC), Canada, measuring the co-occurrence of encephalopathy and overdose is challenging due to lack of standardized screening. We aimed to estimate the prevalence of encephalopathy among people who experienced a drug poisoning event and examine the association between drug poisoning and encephalopathy.Methods Using a 20% random sample of BC residents from administrative health data, we conducted a cross-sectional analysis. Drug poisoning events were identified using the Provincial Overdose Cohort definition and encephalopathy was identified using ICD codes from hospitalization, emergency department, and primary care records between January 1st 2015 and December 31st 2019. Unadjusted and adjusted log-binomial regression models were employed to estimate the risk of encephalopathy among people who had a drug poisoning event compared to people who did not experience a drug poisoning event.Results Among people with encephalopathy, 14.6% (n = 54) had one or more drug poisoning events between 2015 and 2019. After adjusting for sex, age, and mental illness, people who experienced a drug poisoning were 15.3 times (95% CI = 11.3, 20.7) more likely to have encephalopathy compared to people who did not experience a drug poisoning event. People who were 40 years and older, male, and had a mental illness were at increased risk of encephalopathy.Conclusions There is a need for collaboration between health care providers, experts, and key stakeholders to develop a standardized approach to define, screen, and detect neurocognitive injury related to illicit drug poisoning.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".