Absence of Quantitative Improvement in Neuropsychological Profiles in Patients Who Exhibit Moderate Brain Impairment: Comparisons of Cross-Sectional and Longitudinal Data (1 through 4 Years Post-Injury)
Bibliographic record
Abstract
A total of 127 adult patients who had sustained an impact of significant mechanical energy to their skulls during motor vehicle incidents were given thorough neuropsychological, cognitive and personality assessments between 0.5 years and 4 years after the event. Cross-sectional analysis indicated no statistically significant objective changes in patients as a function of yearly intervals. However there was strong evidence of significant deterioration of neuropsychological proficiency and efficiency between 0.3 to 1.0 years after the injury. A subset (n = 20) of patients who displayed moderately severe neuropsychological impairment when assessed about 1 year after the injury showed no statistically significant changes when reassessed about 1.5 years later (2.5 years after the brain trauma). These results challenge the traditional concept of “recovery” following a traumatic brain injury and indicate that insidious processes that adversely affect neurocognitive capacity may emerge 0.5 years after the trauma. Post-hoc analysis indicated that the occurrence of unconsciousness or its duration at the time of the injury minimally affected the magnitude of subsequent indices of neuropsychological impairment but influenced the incidence of electroencephalographic theta activity during the years following the injury.
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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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".