A-09 Concussion History May Accelerate Cognitive Decline in Older Adults
Bibliographic record
Abstract
Abstract Objective Concussion is a common occurrence among older adults, stemming largely from falls. Evidence suggests that history of moderate–severe traumatic brain injury (TBI) increases risk for cognitive decline and dementia; however, long-term outcomes associated with concussion remain unclear. This study aims to investigate longitudinal cognitive change among older adults with self-reported concussion history (CH). Method Older adults (n = 39) enrolled in an observational, longitudinal study by the Center for Neurodegeneration and Translational Neuroscience diagnosed with mild cognitive impairment or Alzheimer’s disease were studied, including 14 with CH. Participants completed baseline and one-year follow-up testing, including the Montreal Cognitive Assessment (MoCA). Repeated measures ANCOVA with age and education covariates assessed change in MoCA Total Scores from baseline to follow-up based on CH. Results Main effects for age, education, time, and CH were not significant; however, significant interaction for CH by time was revealed, F(1,34) = 4.46, p < .05 such that those with CH demonstrated significantly greater decline from baseline to follow-up than those without CH (p < .05). In the CH group, change over time was associated with an effect size of 1.20 (Cohen’s d) compared to an effect size of 0.22 in the non-CH group. Conclusions History of concussion may lead to accelerated rate of cognitive decline in those diagnosed with MCI and AD over a 1-year period, which is consistent with prior research in moderate–severe TBI. These results preliminarily support the notion that concussion may be associated with significantly worse cognitive outcomes among older adults. Confirmation of our findings in larger samples and prospective validation of the observation are warranted.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".