Impact of physiological and neuropsychological function on quality of life in traumatic brain injury patients
Bibliographic record
Abstract
Traumatic Brain Injury (TBI) leads to neuropsychological and social impairments, affecting quality of life (QoL). This study examines how heart rate variability (HRV) and neuropsychological functions under different cognitive task conditions influence TBI patients’ QoL. The study adopted 20 healthy and 11 TBI participants. Measurements included the Montreal Cognitive Assessment (MoCA), Community Mental Status Examination (CMSE), Continuous Performance Test (CPT-3), Tower of London, Wisconsin Card Sorting Test, Rey Complex Figure Test, WAIS-4, Fatigue Severity Scale, the WHO QoL Taiwan Brief Version (WHOQOL) and Well-Being Index (WHO-5), Hierarchy of Care Required, Daily Executive Behavior Scale, Perceived Family Relationship Scale, Connor-Davidson Resilience Scale, and Center for Epidemiologic Studies Depression Scale (CESD). The HRV indexes were measured by biofeedback device. Statistical analyses include independent t-test, Chi-square, repeated measure ANOVA, Pearson correlation, and mediation analysis. TBI group showed lower scores in memory, attention, executive function, and psychosocial measures. HRV results revealed lower baseline LF and task-related interaction effects in LF, LF/HF, and RMSSD. Mediation analysis further showed that CPT-3 performance and LF mediated the group’s impact on self-reported quality of life. TBI patients exhibit physiological, neuropsychological, and psychosocial impairments, which are closely related to their QoL. In particular, the preservation response in the CPT-3 and LF showed significant predictive effects on QoL.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".