Comparing Composite Scores for the ANAM4 TBI-MIL for Research in Mild Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: The Automated Neuropsychological Assessment Metrics (Version 4) Traumatic Brain Injury Military (ANAM4 TBI-MIL) is commonly administered among U.S. service members both pre-deployment and following TBI. The current study used the ANAM4 TBI-MIL to develop a cognition summary score for TBI research and clinical trials, comparing eight composite scores based on their distributions and sensitivity/specificity when differentiating between service members with and without mild TBI (MTBI). METHOD: Male service members with MTBI (n = 56; Mdn = 11 days-since-injury) or no self-reported TBI history (n = 733) completed eight ANAM4 TBI-MIL tests. Their throughput scores (correct responses/minute) were used to calculate eight composite scores: the overall test battery mean (OTBM); global deficit score (GDS); neuropsychological deficit score-weighted (NDS-W); low score composite (LSC); number of scores <50th, ≤16th percentile, or ≤5th percentile; and the ANAM Composite Score (ACS). RESULTS: The OTBM and ACS were normally distributed. Other composites had skewed, zero-inflated distributions (62.9% had GDS = 0). All composites differed significantly between participants with and without MTBI (p < .001), with deficit scores showing the largest effect sizes (d = 1.32-1.47). The Area Under the Curve (AUC) was lowest for number of scores ≤5th percentile (AUC = 0.653) and highest for the LSC, OTBM, ACS, and NDS-W (AUC = 0.709-0.713). CONCLUSIONS: The ANAM4 TBI-MIL has no well-validated composite score. The current study examined multiple candidate composite scores, finding that deficit scores showed larger group differences than the OTBM, but similar AUC values. The deficit scores were highly correlated. Future studies are needed to determine whether these scores show less redundancy among participants with more severe TBIs.
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How this classification was reachedexpand
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".