Race, Ethnicity, and Management of Pain from Long‐bone Fractures: A Prospective Study of Two Academic Urban Emergency Departments
Bibliographic record
Abstract
OBJECTIVES: The objective was to test the hypothesis that African American and Hispanic patients are less likely to receive analgesics than white patients in two academic urban emergency departments (EDs). METHODS: This was a prospective observational study of a convenience sample of patients with long-bone fractures from April 2002 to November 2006 in two academic urban EDs. Eligibility criteria were age 18-55 years, isolated long-bone fracture, and race and ethnicity (Hispanic, African American, and white). The primary outcome was receipt of analgesics; secondary outcomes included receipt of opioids, dose, route, time to first analgesic, and change in pain. Logistic regression was used to adjust the risk of receiving analgesics for patients' initial rating of pain and demographic characteristics. RESULTS: Of 1,239 patients with suspected long-bone fractures, 345 patients were eligible: 177 (51%) were Hispanic, 98 (28%) were African American, and 70 (20%) were white. Administration of analgesics was not associated with race or ethnicity. Sixteen percent (95% confidence interval [CI] = 11% to 22%) of Hispanic, 15% (95% CI = 10% to 24%) of African American, and 14% (95% CI = 8% to 24%) of white patients did not receive any analgesics. Seventy-four percent of Hispanic (95% CI = 67% to 80%), 66% of African American (95% CI = 57% to 75%), and 69% (95% CI = 57% to 78%) of white patients received opioid analgesics. After adjustment for covariates, there was no evidence of an association between receipt of analgesics or opioid analgesics and the race or ethnicity of the patients. There were no significant differences in time to treatment, dose, route, or change in pain. CONCLUSIONS: Receipt of analgesics for pain from long-bone fractures was not associated with patient race or ethnicity in two academic urban EDs.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".