Interventions to Improve Cognitive Outcomes in Older Adults with Traumatic Brain Injury and Association Between Social Determinants of Health and Intervention Effectiveness: A Scoping Review
Bibliographic record
Abstract
Background: At least one million Canadians are at risk of experiencing a traumatic brain injury (TBI) in later life, which can lead to cognitive decline. We identified interventions studied to improve cognitive outcomes in older adults with TBI, and examined how social determinants of health (SDoH) may influence their effectiveness. Methods: We followed JBI guidance and searched five electronic databases from inception until March 2023 for studies evaluating the clinical and cost effectiveness, and safety of interventions being studied to improve cognitive outcomes in older adults with TBI. We abstracted SDoH following the PROGRESS-Plus framework. Results: We included 20 studies and 44,462 predominantly men/male (65%) participants with a mean age of 65.9 years; studies reported 51 cognitive outcomes. Three studies reported on race or ethnicity, eight studies reported on education, and no studies differentiated gender from sex. No studies reported on the association between SDoH and intervention effectiveness. We did not identify any economic evaluations. We classified 10 interventions into four categories: non-pharmacologic cognitive strategies (number of studies [n]=16), medications (n=1), non-invasive procedures (n=2), and invasive procedures (n=1). Invasive procedures and non-pharmacologic cognitive strategies had a statistically significant positive effect on cognitive measures in one and seven studies, respectively. Non-invasive procedures (n=2) did not have significant cognitive effects. Use of hypnotics (benzodiazepines and non-benzodiazepines) was significantly associated with a shorter time to incident dementia in one study. Conclusion: Non-pharmacologic cognitive strategies were the most-studied interventions for improving cognitive outcomes in older adults with TBI. Future research should better integrate a health equity lens and standardize outcome measurement.
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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 | 0.017 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".