Impact of Physician and Patient Gender on Pain Management in the Emergency Department—A Multicenter Study
Bibliographic record
Abstract
OBJECTIVE: Pain is a complex experience influenced by factors such as age, race, and ethnicity. We conducted a multicenter study to better understand emergency department (ED) pain management practices and examined the influence of patient and provider gender on analgesic administration. DESIGN: Prospective, multicenter, observational study. SETTING: Consecutive patients, >or=8-years-old, presenting with complaints of moderate to severe pain (pain numerical rating scale [NRS] > 3) at 16 U.S. and three Canadian hospitals. OUTCOMES MEASURES: Receipt of any ED analgesic, receipt of opioids, and adequate pain relief in the ED. RESULTS: Eight hundred forty-two patients participated including 56% women. Baseline pain scores were similar in both genders. Analgesic administration rates were not significantly different for female and male patients (63% vs 57%, P = 0.08), although females presenting with severe pain (NRS >or=8) were more likely to receive analgesics (74% vs 64%, P = 0.02). Female physicians were more likely to administer analgesics than male physicians (66% vs 57%, P = 0.009). In logistic regression models, predictors of ED analgesic administration were male physician (odds ratio [OR] = 0.7), arrival pain (OR = 1.3), number of pain assessments (OR = 1.83), and charted follow-up plans (OR = 2.16). With regard to opioid administration, female physicians were more likely to prescribe opioids to females (P = 0.006) while male physicians were more likely to prescribe to males (P = 0.05). In logistic regression models, predictors of opioids administration included male patient gender (OR = 0.58), male patient-physician interaction (OR = 2.58), arrival pain score (OR = 1.28), average pain score (OR = 1.10), and number of pain assessments (OR = 1.5). Pain relief was not impacted by gender. CONCLUSION: Provider gender as opposed to patient gender appears to influence pain management decisions in the ED.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".