Initial Management of Trauma by a Trauma Team: Effect on Timeliness of Care in a Teaching Hospital
Bibliographic record
Abstract
The objective of this study was to determine if timeliness of care would improve after implementation of the team approach in trauma management in a single teaching hospital. To make this determination, we used a before-and-after retrospective cohort series for a 550-bed teaching and tertiary referral hospital that was not a level 1 trauma center. We included all patients who presented to the Emergency Department and who were admitted to St. Paul's Hospital because of trauma during 2 baseline months (May and November 1987; n = 111) and 2 follow-up months (May and November 1990; n = 142). In 1988, a formal trauma team was developed to coordinate the care of trauma patients who were seen in the Emergency Department. Indications for calling the trauma team were based on the criteria of the American College of Surgeons for triage to a trauma center. We calculated elapsed time from assessment in the Emergency Department to arrival of the trauma surgeon, discharge from the Emergency Department, and arrival of the patient in the operating room (for urgent or emergent surgery). We also determined the Revised Trauma Score, the Injury Severity Score (1985 version), the crude mortality ratio, and the Z statistic (population outcome comparison). After implementation of the trauma team, median elapsed time from initial nursing assessment in the Emergency Department to arrival in the operating Room for blunt trauma patients decreased from 11.33 to 4.82 hours (P = .05), but there were no significant differences in any other measures of timeliness, crude mortality, or adjusted mortality. We conclude that implementation of a trauma team in a teaching hospital is associated with a minimal effect on timeliness of care for admitted trauma patients.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".