Dedicated orthopedic trauma theatres: effect on morbidity and mortality in a single trauma centre.
Bibliographic record
Abstract
BACKGROUND: A general trend in orthopedic traumatology is the advent of daily, dedicated orthopedic trauma theatres. Availability of trauma theatres is believed to decrease morbidity and mortality, but this remains unproven. We performed a retrospective review comparing morbidity and mortality outcomes at a single institution before and after the establishment of a dedicated trauma room. The purpose was to determine whether a change in outcomes occurred for a single routine procedure with known outcome expectations (hemiarthroplasty of the hip after femoral neck fracture) with the implementation of a designated trauma theatre. METHODS: We examined a cohort of 457 elderly patients (245 before and 212 after trauma theatre implementation) who underwent hemiarthroplasty for displaced low-energy subcapital hip fractures. RESULTS: Patients in both groups were similar in terms of age, sex ratio and ASA classification. We found statistically significant differences favouring the dedicated trauma room system for postoperative morbidity. Despite this outcome, the average time to surgery for these patients significantly increased. We noted no difference in mortality between the 2 groups. CONCLUSION: The hip fracture population can be treated safely in the context of dedicated trauma room time; however, there needs to be prioritization of hip fractures in a tertiary care centre or other trauma cases will tend to take precedence.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".