Changes in mortality for the general population and individuals with pre-pandemic acute care for alcohol or opioids during the COVID-19 pandemic in Ontario, Canada
Bibliographic record
Abstract
Rates of alcohol- and opioid-related harms, including emergency department (ED) visits, hospitalizations, and deaths caused by alcohol or opioids, have increased since the beginning of the pandemic.<sup>, </sup> It has been hypothesized that these increases in harms were driven by increased substance use by individuals with a prepandemic alcohol-use disorder (AUD) or opioid-use disorder (OUD) rather than by greater substance use during the pandemic in the general population.<sup>,</sup> Despite the face validity of these concerns, there is limited data on changes in mortality for individuals with prepandemic substance use disorders (SUDs). This study compared excess mortality during the COVID-19 pandemic between the general population and individuals who received acute care (ED visit or hospitalization) for alcohol or opioids prepandemic.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".