Opioid prescribing requirements to minimize unused medications after an emergency department visit for acute pain: a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Unused opioid prescriptions can be a driver of opioid misuse. Our objective was to determine the optimal quantity of opioids to prescribe to patients with acute pain at emergency department discharge, in order to meet their analgesic needs while limiting the amount of unused opioids. METHODS: In a prospective, multicentre cohort study, we included consecutive patients aged 18 years and older with an acute pain condition present for less than 2 weeks who were discharged from emergency department with an opioid prescription. Participants completed a pain medication diary for real-time recording of quantity, doses, and names of all analgesics consumed during a 14-day follow-up period. RESULTS: < 0.001). Most opioid tablets prescribed (63%) were unused. To meet the opioid need of 80% of patients for 2 weeks, we found that those experiencing renal colic or abdominal pain required fewer opioid tablets (8 morphine 5 mg tablet equivalents) than patients who had fractures (24 tablets), back pain (21 tablets), neck pain (17 tablets), or other musculoskeletal pain (16 tablets). INTERPRETATION: Two-thirds of opioid tablets prescribed at emergency department discharge for acute pain were unused, whereas opioid requirements varied significantly based on the cause of acute pain. Smaller, cause-specific opioid prescriptions could provide adequate pain management while reducing the risk of opioid misuse. TRIAL REGISTRATION: ClinicalTrials.gov, no. NCT03953534.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 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".