Non-neurologic organ dysfunction in severe traumatic brain injury*
Bibliographic record
Abstract
OBJECTIVE: To describe the incidence of non-neurologic organ dysfunction and its association with outcome in patients with severe traumatic brain injury admitted to intensive care. DESIGN: Observational cohort study. SETTING: Foothills Medical Centre, which is the only neurosurgical service in southern Alberta (population approximately 1.3 million). PATIENTS: Patients were 209 consecutive patients with severe traumatic brain injury. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Non-neurologic organ dysfunction was measured by the maximum modified multiple organ dysfunction score. Organ system failure was defined as a component score of >/=3 on any day during the patient's intensive care unit stay. One hundred and eighty-five patients (89%) developed dysfunction of at least one non-neurologic organ system. Ninety-six organ system failures were identified in 74 patients (35%). Respiratory failure was the most common non-neurologic organ system failure, occurring in 23% of patients, whereas cardiovascular failure occurred in 18%. Eight patients (4%) had failure of the coagulation system. One patient had renal failure, whereas no patient developed hepatic failure. In a multivariate model, non-neurologic organ dysfunction was independently associated with hospital mortality (odds ratio for hospital mortality, 1.63; 95% confidence interval, 1.34, 1.98 for one maximum modified multiple organ dysfunction score point). Non-neurologic organ dysfunction was also independently associated with dichotomized Glasgow Outcome Score, as a measure of neurologic outcome (odds ratio for unfavorable neurologic outcome, 1.53; 95% confidence interval, 1.22, 1.98 for one maximum modified multiple organ dysfunction score point). The timing of the organ dysfunction did not appear to be important in the prediction of outcome. CONCLUSIONS: Non-neurologic organ dysfunction is common in patients with severe traumatic brain injury and is independently associated with worse outcome.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".