NIH toolbox cognition tests following traumatic brain injury: Frequency of low scores.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.014 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it