NIH toolbox cognition tests following traumatic brain injury: Frequency of low scores.
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: To apply multivariate base rate analyses to the National Institutes of Health Toolbox Cognition Battery (NIHTB-CB) to facilitate the identification of cognitive impairment in individuals with traumatic brain injury (TBI). Research Method/Design: In a multisite cross-sectional design, 158 participants who sustained a complicated mild or moderate TBI (n = 74) or severe TBI (n = 84) at least 1 year earlier were administered the NIHTB-CB. The NIHTB-CB is comprised of 2 crystallized cognition tests (reflecting premorbid ability) and 5 fluid cognition tests, measuring processing speed, memory, and executive functioning. Base rates for obtaining 0 to 5 low fluid cognition scores were calculated across a range of cutoffs for defining a low test score (≤25th to 5th percentiles). Base rates of low scores in the TBI sample were compared to the NIHTB-CB normative sample using diagnostic accuracy statistics. RESULTS: The proportion of the TBI sample obtaining low scores decreased as the cutoff for defining a low score decreased. Individuals with lower premorbid cognitive ability, as measured by NIHTB-CB Crystallized Composite score, tended to produce more low scores on the NIHTB-CB fluid cognition tests, even when using fully demographically adjusted scores. Certain patterns of low scores were associated with TBI (defined as likelihood ratio >2.0), whereas others were nonspecific, occurring almost as often in participants without TBI. CONCLUSIONS/IMPLICATIONS: Premorbid ability stratified base rate tables provided in this article can guide researchers and clinicians in the interpretation of NIHTB-CB performance in adults with TBI. (PsycINFO Database Record
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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 | 0.002 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".