Social Support Buffers Against Cognitive Decline in Single Mild Traumatic Brain Injury With Loss of Consciousness: Results From the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVES: We investigated rates of cognitive decline at 3-year follow-up from initial examination in people reporting mild traumatic brain injury (mTBI) with loss of consciousness (LOC) more than a year prior to initial examination. We examined the role of social support as predictor of preserved cognitive function in this sample. METHOD: Analyses were conducted on 440 participants who had self-reported LOC of <1 min, 350 with LOC of 1-20 min, and 10,712 healthy controls, taken from the Canadian Longitudinal Study on Aging (CLSA), a nationwide study on health and aging. RESULTS: People who reported at baseline that they had experienced mTBI with LOC of 1-20 min more than a year prior were 60% more likely to have experienced global cognitive decline than controls at three-year follow-up. Cognitive decline was most apparent on measures of executive functioning. Logistic regression identified increased social support as predictors of relatively preserved cognitive function. DISCUSSION: mTBI with longer time spent unconscious (i.e., LOC 1-20 min) is associated with greater cognitive decline years after the head injury. Perceived social support, particularly emotional support, may help buffer against this cognitive decline.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".