Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To measure the variation in trauma center mortality across Canadian trauma systems, assess the contribution of traumatic brain injury and thoracoabdominal injury to observed variations, and evaluate whether the presence of recommended trauma system components is associated with mortality. SUMMARY BACKGROUND DATA: Injuries represent one of the leading causes of mortality, disability, and health care costs worldwide. Trauma systems have improved injury outcomes, but the impact of trauma system configuration on mortality is unknown. METHODS: We conducted a retrospective cohort study of adults admitted for major injury to trauma centers across Canada (2006-2012). Multilevel logistic regression was used to estimate risk-adjusted hospital mortality and assess the impact of 13 recommended trauma system components. RESULTS: Of 78,807 patients, 8382 (10.6%) died in hospital including 6516 (78%) after severe traumatic brain injury and 749 (9%) after severe thoracoabdominal injury. Risk-adjusted mortality varied from 7.0% to 14.2% across provinces (P < 0.0001); 11.1% to 26.0% for severe traumatic brain injury (P < 0.0001), and 4.7% to 5.9% for thoracoabdominal injury (P = 0.2). Mortality decreased with increasing number of recommended trauma system elements; adjusted odds ratio = 0.93 (0.87-0.99). CONCLUSIONS: We observed significant variation in trauma center mortality across Canadian provinces, specifically for severe traumatic brain injury. Provinces with more recommended trauma system components had better patient survival. Results suggest that trauma system configuration may be an important determinant of injury mortality. A better understanding of which system processes drive optimal outcomes is required to reduce the burden of injury worldwide.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it