Comparing physician and advanced care provider use of opioids for treatment of acute chest pain in the emergency department
Bibliographic record
Abstract
BACKGROUND: Acute chest pain is a common and high-stakes emergency department (ED) presentation, frequently associated with acute coronary syndrome (ACS). Opioids such as morphine and fentanyl are used when symptoms persist despite nitrates, but little is known about whether prescribing differs between advanced care providers (ACPs) and physicians. OBJECTIVE: To compare intravenous (IV) morphine and fentanyl prescribing patterns for ACS-related chest pain between ACPs and physicians in an academic ED. METHODS: We performed a retrospective study of adult patients with ACS presenting to an academic ED from January 2021 to January 2024. The primary outcome was IV morphine administration; the secondary outcome was IV fentanyl administration. Multivariable logistic regression adjusted for demographics, clinical factors, and nitroglycerin administration prior to opioid. Firth's penalized logistic regression was conducted as sensitivity analysis. RESULTS: Among 2055 patients, 154 (7.5 %) were treated by ACPs and 1901 (92.5 %) by physicians. Morphine use was similar between groups (42.2 % vs 42.8 %, p = 0.93), while fentanyl was less common with ACPs (5.8 % vs 11.6 %, p = 0.03). In adjusted models, ACP vs Physician status was not significantly associated with morphine (OR 0.78, 95 % CI 0.49-1.24) or fentanyl use (OR 1.10, 95 % CI 0.50-2.49). Nitroglycerin prior to opioid was strongly associated with reduced opioid administration. Sensitivity analyses confirmed these findings. CONCLUSION: Opioid prescribing for ACS-related chest pain did not differ significantly between ACPs and physicians after adjustment. Prescribing decisions were driven more by patient factors and treatment sequencing, supporting comparable roles for ACPs and physicians in ED ACS pain management.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".