Association between toxic drug events and encephalopathy in British Columbia, Canada: a cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Encephalopathy can occur from a non-fatal toxic drug event (overdose) which results in a partial or complete loss of oxygen to the brain, or due to long-term substance use issues. It can be categorized as a non-traumatic acquired brain injury or toxic encephalopathy. In the context of the drug toxicity crisis in British Columbia (BC), Canada, measuring the co-occurrence of encephalopathy and drug toxicity is challenging due to lack of standardized screening. We aimed to estimate the prevalence of encephalopathy among people who experienced a toxic drug event and examine the association between toxic drug events and encephalopathy. METHODS: Using a 20% random sample of BC residents from administrative health data, we conducted a cross-sectional analysis. Toxic drug events were identified using the BC 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 toxic drug event compared to people who did not experience a toxic drug event. RESULTS: Among people with encephalopathy, 14.6% (n = 54) had one or more drug toxicity events between 2015 and 2019. After adjusting for sex, age, and mental illness, people who experienced drug toxicity were 15.3 times (95% CI = 11.3, 20.7) more likely to have encephalopathy compared to people who did not experience a drug toxicity 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 community members, health care providers, and key stakeholders to develop a standardized approach to define, screen, and detect neurocognitive injury related to drug toxicity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".