Bibliographic record
Abstract
COVID-19 is compounding opioid use disorder throughout the United States. While recent commentaries provide useful policy recommendations, few studies examine the intersection of COVID-19 policy responses and patterns of opioid overdose. We examine opioid overdoses prior to and following the Pennsylvania stay-at-home order implemented on April 1, 2020. Using data from the Pennsylvania Overdose Information Network, we measure change in monthly incidents of opioid-related overdose pre- versus post-April 1, and the significance of change by gender, age, race, drug class, and naloxone doses administered. Findings demonstrate statistically significant increases in overdose incidents among both men and women, White and Black groups, and several age groups, most notably the 30–39 and 40–49 ranges, following April 1. Significant increases were observed for overdoses involving heroin, fentanyl, fentanyl analogs or other synthetic opioids, pharmaceutical opioids, and carfentanil. The study em...
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".