Moderate-Severe TBI as a Progressive Disorder: Patterns and Predictors of Cognitive Declines in the Chronic Stages of Injury
Bibliographic record
Abstract
BACKGROUND: Moderate-severe traumatic brain injury (TBI) has been associated with progressive cognitive decline in the chronic injury stages in a small number of studies. OBJECTIVE: This study aimed to (i) replicate our previous findings of decline from 1 to 3+ years post-injury in a larger, non-overlapping sample and (ii) extend these findings by examining the proportion of decliners in 2 earlier time windows, and by investigating novel predictors of decline. METHODS: N = 48 patients with moderate-severe TBI underwent neuropsychological assessment at 2, 5, 12 months, and 30+ months post-injury. We employed the Reliable Change Index (RCI) to evaluate decline, stability and improvement across time and logistic regression to identify predictors of decline (demographic/cognitive reserve; injury-related). RESULTS: The proportions of patients showing decline were: 12.5% (2-5 months post-injury), 17% (5-12 months post-injury), and 27% (12-30+ months post-injury). Measures of verbal retrieval were most sensitive to decline. Of the predictors, only left progressive hippocampal volume loss from 5 to 12 months post-injury significantly predicted cognitive decline from 12 to 30+ months post-injury. CONCLUSIONS: Identical to our previous study, 27% of patients declined from 12 to 30+ months post-injury. Additionally, we found that the further from injury, the greater the proportion of patients declining. Importantly, earlier progressive hippocampal volume loss predicted later cognitive decline. Taken together, the findings highlight the need for ongoing research and treatment that target these deleterious mechanisms affecting patients in the chronic stages of moderate-severe 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 | 0.001 | 0.003 |
| 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".