Evaluating and Updating the IMPACT Model to Predict Outcomes in Two Contemporary North American Traumatic Brain Injury Cohorts
Bibliographic record
Abstract
The International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury (IMPACT) model is a widely recognized prognostic model applied after traumatic brain injury (TBI). However, it was developed with patient cohorts that may not reflect modern practice patterns in North America. We analyzed data from two sources: the placebo arm of the phase II double-blinded, multicenter, randomized controlled trial Prehospital Tranexamic Acid for TBI (TXA) cohort and an observational cohort with similar inclusion/exclusion criteria (Predictors of Low-risk Phenotypes after Traumatic Brain Injury Incorporating Proteomic Biomarker Signatures [PROTIPS] cohort). All three versions of the IMPACT model-core, extended, and laboratory-were evaluated for 6-month mortality (Glasgow Outcome Scale Extended [GOSE] = 1) and unfavorable outcomes (GOSE = 1-4). Calibration (intercept and slope) and discrimination (area under the receiver operating characteristic curve [ROC-AUC]) were used to assess model performance. We then compared three model updating methods-recalibration in the large, logistic recalibration, and coefficient update-with the best update method determined by likelihood ratio tests. In our calibration analysis, recalibration improved both intercepts and slopes, indicating more accurate predicted probabilities when recalibration was done. Discriminative performance of the IMPACT models, measured by AUC, showed mortality prediction ROCs between 0.61 and 0.82 for the TXA cohort, with the coefficient updated Lab model achieving the highest at 0.84. Unfavorable outcomes had lower AUCs, ranging from 0.60 to 0.79. Similarly, in the PROTIPS cohort, AUCs for mortality ranged from 0.75 to 0.82, with the coefficient updated Lab model also showing superior performance (AUC 0.84). Unfavorable outcomes in this cohort presented AUCs from 0.67 to 0.73, consistently lower than mortality predictions. The closed testing procedure using likelihood ratio tests consistently identified the coefficient update model as superior, outperforming the original and recalibrated models across all cohorts. In our comprehensive evaluation of the IMPACT model, the coefficient updated models were the best performing across all cohorts through a structured closed testing procedure. Thus, standardization of model updating procedures is needed to reproducibly determine the best performing versions of IMPACT that reflect the specific characteristics of a dataset.
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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 | 0.036 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".