The application of IMPACT prognostic models to elderly adults with traumatic brain injury: A population-based observational cohort study
Bibliographic record
Abstract
OBJECTIVE: To examine the performance of the International Mission for Prognosis and Clinical Trial Design in Traumatic Brain Injury (IMPACT) prognostic models in older patients. METHODS: Using data from the National Study on Costs and Outcomes of Trauma (NSCOT), this study identified adult patients presenting to US hospitals in 2001 and 2002 with non-penetrating moderate or severe traumatic brain injury (GCS ≤ 12). IMPACT model calibration and discrimination in the older stratum (65-84 years) was compared to that in the younger stratum (18-64 years). RESULTS: IMPACT model discrimination did not differ significantly between the older (n = 202; weighted n = 268) and younger strata (n = 613; weighted n = 1632) and was generally adequate (c-statistic for the core-death model = 0.81 [0.77-0.84] vs 0.75 [0.66-0.84], respectively; p = 0.26). IMPACT model calibration was poor for both older and younger strata (Hosmer-Lemeshow p-value for the core-death model = 0.01 vs < 0.0001, respectively). Pre-specified qualitative graphical evaluation suggested substantial under-prediction of mortality in the oldest decades of life, but not among younger patients. CONCLUSIONS: The examined IMPACT prognostic models demonstrated adequate discrimination and poor calibration in both older and younger patients, yet particular caution may be required when applying these models to the elderly.
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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 | 0.019 | 0.051 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".