Factors associated with unfavorable outcomes in older patients with traumatic brain injury: analysis from the “All of Us” research program
Bibliographic record
Abstract
Traumatic brain injury (TBI) afflicts approximately 70 million people worldwide annually, with patients aged 65 years and older accounting for an increasing proportion of TBI patients. Older patients also experience increased morbidity and mortality post-TBI compared to their younger counterparts. Nevertheless, clinical trials often exclude older TBI patients, and age-specific TBI treatment is lacking. We hypothesized that the APOE genotype and age-associated comorbidities, such as heart disease, are associated with unfavorable outcomes following TBI in older patients. We utilized a dataset from the “All of Us research” (AoU) to study this vulnerable population post-TBI. Launched by the National Institutes of Health (NIH), AoU is a nationwide prospective cohort study aiming to enroll 1 million or more individuals by emphasizing traditionally underrepresented populations in the United States. We defined patients diagnosed with post-concussion syndromes (PCS) as those with unfavorable TBI outcomes, and we also assessed the associations between PCS observed in older patients and different comorbidities variables/APOE genotypes via multiple logistic regression models. Consequently, APOE ε4 allele was strongly associated with PCS in patients aged 65 and older. Our findings provide direct evidence for developing better predictive tools and potentially improving the clinical guidance and management of older adults with 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 | 0.001 |
| 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 | 0.001 |
| 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".