Predicting Outcomes in Patients with Diffuse Axonal Injury: External Validation of the Widely Used Prognostic Instruments
Bibliographic record
Abstract
AIM: Accurate prognosis of diffuse axonal injury (DAI) is important in directing clinical care, allocating resources appropriately, and communicating with families and surrogate decision-makers. METHODS: A study was conducted on patients with clinical DAI due to closed-head traumatic brain injury treated at a trauma center in Brazil from July 2013 to September 2015. The objective efficacy of the Glasgow Coma Scale (GCS), Trauma and Injury Severity Scoring system (TRISS), New Trauma and Injury Severity Scoring system (NTRISS), Abbreviated Injury Scale (AIS)/head, Corticosteroid Randomization After Significant Head Injury (CRASH), and International Mission on Prognosis and Analysis of Clinical Trials (IMPACT) models in the prediction of mortality at 14 days and 6-months and unfavorable outcomes at 6 months was tested. RESULTS: Our cohort comprised 95 prospectively recruited adults (85 males, 10 females, mean age 30.3 ± 10.9 years) admitted with DAI. Model efficacy was assessed through discrimination (area under the curve [AUC]), and Cox calibration. The AIS/head, TRISS, NTRISS, CRASH, and IMPACT models were able to discriminate both mortality and unfavorable outcomes (AUC 0.78-0.87). IMPACT models resulted in a statistically perfect calibration for both 6-month outcome variables; mortality and 6-month unfavorable outcome. Calibration also revealed that TRISS, NTRISS, and CRASH systematically overpredicted both outcomes, except for 6-month unfavorable outcome with TRISS. CONCLUSIONS: The results of this study suggest that TRISS, NTRISS, CRASH, and IMPACT models satisfactorily discriminate between mortality and unfavorable outcomes. However, only the TRISS and IMPACT models showed accurate calibration when predicting 6-month unfavorable 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 | 0.020 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".