Transport Time and Mortality in Critically Ill Patients with Severe Traumatic Brain Injury
Bibliographic record
Abstract
PURPOSE: Severe traumatic brain injury (TBI) is a major cause of morbidity and mortality in critically ill patients. Pre-hospital care and transportation time may impact their outcomes. METHODS: Using the British Columbia Trauma Registry, we included 2,860 adult (≥18 years) patients with severe TBI (abbreviated injury scale head score ≥4), who were admitted to an intensive care unit (ICU) in a centre with neurosurgical services from January 1, 2000 to March 31, 2013. We evaluated the impact of transportation time (time of injury to time of arrival at a neurosurgical trauma centre) on in-hospital mortality and discharge disposition, adjusting for age, sex, year of injury, injury severity score (ISS), revised trauma score at the scene, location of injury, socio-economic status and direct versus indirect transfer. RESULTS: Patients had a median age of 43 years (interquartile range [IQR] 26-59) and 676 (23.6%) were female. They had a median ISS of 33 (IQR 26-43). Median transportation time was 80 minutes (IQR 40-315). ICU and hospital length of stay were 6 days (IQR 2-12) and 20 days (IQR 7-42), respectively. Six hundred and ninety-six (24.3%) patients died in hospital. After adjustment, there was no significant impact of transportation time on in-hospital mortality (odds ratio 0.98, 95% confidence interval 0.95-1.01). There was also no significant effect on discharge disposition. CONCLUSIONS: No association was found between pre-hospital transportation time and in-hospital mortality in critically ill patients with severe TBI.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| 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".