Neurocognitive Predictors of Financial Capacity in Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: To develop cognitive models of financial capacity (FC) in patients with traumatic brain injury (TBI). DESIGN: Longitudinal design. SETTING: Inpatient brain injury rehabilitation unit. PARTICIPANTS: Twenty healthy controls, and 24 adults with moderate-to-severe TBI were assessed at baseline (30 days postinjury) and 6 months postinjury. MAIN OUTCOME MEASURES: The FC instrument (FCI) and a neuropsychological test battery. Univariate correlation and multiple regression procedures were employed to develop cognitive models of FCI performance in the TBI group, at baseline and 6-month time follow-up. RESULTS: Three cognitive predictor models of FC were developed. At baseline, measures of mental arithmetic/working memory and immediate verbal memory predicted baseline FCI performance (R = 0.72). At 6-month follow-up, measures of executive function and mental arithmetic/working memory predicted 6-month FCI performance (R = 0.79), and a third model found that these 2 measures at baseline predicted 6-month FCI performance (R = 0.71). CONCLUSIONS: Multiple cognitive functions are associated with initial impairment and partial recovery of FC in moderate-to-severe TBI patients. In particular, arithmetic, working memory, and executive function skills appear critical to recovery of FC in TBI. The study results represent an initial step toward developing a neurocognitive model of FC in patients with TBI.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".