Effects of Trauma Cases on the Care of Patients Who Have Chest Pain in an Emergency Department
Bibliographic record
Abstract
BACKGROUND: Trauma victims sometimes take priority over other patients because their injuries require immediate treatment. We examined whether such demands might compromise the care of patients with acute chest pain in an emergency department. METHODS: Case patients were patients with chest pain who arrived immediately after a major trauma victim. Control patients were patients with chest pain who arrived on a preceding day when no trauma patient was in the emergency department. RESULTS: Case and control patients were similar in mean age (60 vs. 60 years, p = not significant), percentage male (47 vs. 53%, p = not significant) and percentage ultimately diagnosed as cardiac (29 vs. 33%, p = not significant). Case patients spent an average of 81 minutes longer in the emergency department (297 vs. 216 minutes, p = 0.009). Similar delays were observed in the subgroup of patients ultimately diagnosed as cardiac (309 vs. 217 minutes, p = 0.029). Case patients had generally worse scores on the American College of Emergency Physicians Quality Assurance Index (75.6 vs. 84.4, p = 0.027), particularly those ultimately diagnosed as cardiac (60.3 vs. 85.1, p = 0.002). The common failures were failure to administer aspirin, undertreatment of ongoing pain, and failure to provide instructions regarding treatment and need to return. CONCLUSION: Trauma victims can decrease the timeliness and quality of care for other patients who have potentially life-threatening conditions in an emergency department.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".