Harms of prescription opioid use in the United States
Bibliographic record
Abstract
BACKGROUND: Consumption levels of prescription opioids (POs) have increased substantially worldwide, particularly the United States. An emerging perspective implicates increasing consumption levels of POs as the primary system level driving factor behind the observed PO-related harms. As such, the present study aimed to assess the correlations between consumption levels of POs and PO-related harms, including non-medical prescription opioid use (NMPOU), PO-related morbidity and PO-related mortality. FINDINGS: Pearson's product-moment correlations were computed using published data from the United States (2001 - 2010). Consumption levels of POs were extracted from the technical reports published by the International Narcotics Control Board, while data for NMPOU was utilized from the National Survey on Drug Use and Health. Additionally, data for PO-related morbidity (substance abuse treatment admissions per 10,000 people) and PO-related mortality (PO overdose deaths per 100,000 people) were obtained from published studies. Consumption levels of POs were significantly correlated with prevalence of NMPOU in the past month (r =0.741, 95% CI =0.208-0.935), past year (r =0.638, 95% CI =0.014-0.904) and lifetime (r =0.753, 95% CI =0.235-0.938), as well as average number of days per person per year of NMPOU among the general population (r =0.900, 95% CI =0.625-0.976) and NMPOU users (r =0.720, 95% CI =0.165-0.929). Similar results were also obtained for PO-related morbidity and PO-related mortality measures. CONCLUSION: These findings suggest that reducing consumption levels of POs at the population level may be an effective strategy to limit PO-related harms.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".