Association between fentanyl treatment for acute pain in the emergency department and opioid use two weeks after discharge
Bibliographic record
Abstract
BACKGROUND: Analgesia with fentanyl can be associated with hyperalgesia (higher sensitivity to pain) and can contribute to escalating opioid use. Our objective was to assess the relationship between emergency department (ED) acute pain management with fentanyl compared to other opioids, and the quantity of opioids consumed two-week after discharge. We hypothesized that the quantity of opioids consumed would be higher for patients treated with fentanyl compared to those treated with other opioids. METHODS: Patients were selected from two prospective cohorts assessing opioids consumed after ED discharge. Patients ≥18 years treated with an opioid in the ED for an acute pain condition (≤2 weeks) and discharged with an opioid prescription were included. Patients completed a 14-day paper or electronic diary of pain medication use. Quantity of 5 mg morphine equivalent tablets consumed during a 14-day follow-up by patients treated with fentanyl compared to those treated with other opioids during their ED stay were analyzed using a multiple linear regression and propensity scores. RESULTS: We included 707 patients (mean age ± SD: 50 ± 15 years, 47% women) in this study. During follow-up, patients treated with fentanyl (N = 91) during their ED stay consumed a median (IQR) of 5.8 (14) 5 mg morphine equivalent pills compared to 7.0 (14) for those treated with other opioids (p = 0.05). Results were similar using propensity score sensitivity analysis. However, after adjusting for confounding variables, ED fentanyl treatment showed a trend, but not a statistically significant association with a decreased opioid consumption during the 14-day follow-up (B = -2.4; 95%CI = -5.3 to 0.4; p = 0.09). CONCLUSIONS: Patients treated with fentanyl during ED stay did not consume more opioids after ED discharge, compared to those treated with other opioids. If fentanyl does cause more hyperalgesia compared to other opioids, it does not seem to have a significant impact on opioid consumption after ED discharge.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".