Opioid Use and Misuse Three Months After Emergency Department Visit for Acute Pain
Bibliographic record
Abstract
BACKGROUND: Studies evaluating long-term prescription opioid use are retrospective and based on filled opioid prescriptions from governmental databases. These studies cannot evaluate if opioids were really consumed and are unable to differentiate if they were used for a new pain or chronic pain or were misused. The aim of this study was to assess opioid use rate and reasons for consuming 3 months after being discharged from the emergency department (ED) with an opioid prescription. METHODS: This is a prospective cohort study conducted in the ED of a tertiary care urban center with a convenience sample of discharged patients ≥ 18 years who consulted for an acute pain condition (≤2 weeks). Three months post-ED visit, participants were interviewed by phone on their past 2-week opioid consumption and their reasons for consuming: a) for pain related to the initial ED visit, b) for a new unrelated pain, or c) for another reason. RESULTS: Of the 524 participants questioned at 3 months (mean ± SD age = 51 ± 16 years, 47% women), 47 patients (9%, 95% confidence interval [CI] = 7%-12%) reported consuming opioids in the previous 2 weeks. Among those, 34 (72%) reported using opioids for their initial pain, nine (19%) for a new unrelated pain and four (9%) for another reason (0.8%, 95% CI = 0.3%-2.0%, of the whole cohort). Patients who used opioids during the 2 weeks after the ED visit were 3.8 (95% CI = 1.2-12.7) times more likely to consume opioids at 3 months. CONCLUSION: Opioid use at the 3-month follow-up in ED patients discharged with an opioid prescription for an acute pain condition is not necessarily associated with opioid misuse; 91% of those patients consumed opioids to treat pain. Of the whole cohort, less than 1% reported using opioids for reasons other than pain. The rate of long-term opioid use reported by prescription-filling database studies should not be viewed as a proxy for incidence of opioid misuse.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".