Traumatic brain injury and anger proneness: results from the Atherosclerosis Risk in Communities (ARIC) study
Bibliographic record
Abstract
Background/objective Associations of traumatic brain injury (TBI) with subsequent increased anger proneness have been studied in younger populations, but less is known about potential bidirectional associations between TBI and anger proneness among older populations. This study aimed to investigate bidirectional associations between anger proneness and TBI among community-dwelling participants in the Atherosclerosis Risk in Communities Study. Methods TBI was defined by self-report and ICD-9/10 codes. Anger proneness was defined using the Spielberger Trait Anger Scale. We performed 3 analyses: cross-sectional associations of prior TBI with anger proneness (Visit 2, 1990–1992, N = 13,694), associations of interval TBI with change in anger proneness (Visit 2, 1990–1992 to Visit 4, 1996–1998, N = 9,022), and prospective associations of baseline anger proneness with incident TBI (Visit 2, 1990–1992 to 12/31/2020, N = 11,713). Adjusted Tobit, linear, and Cox-proportional hazards regression models estimated associations, respectively. Results Overall, participants were a mean age of 57 years at Visit 2, 55% were female, and 24% were Black. In cross-sectional analyses, prior TBI was associated with slightly higher anger proneness (β = 0.35, 95% CI = 0.17, 0.54). In change analyses, interval TBI was not significantly associated with change in anger proneness score over time (β = 0.16, 95% CI = −0.16, 0.48). In prospective analyses, increasing baseline anger proneness was not significantly associated with incident TBI (moderate anger proneness: HR = 1.05, 95% CI = 0.95, 1.15; high anger proneness: HR = 1.15, 95% CI = 0.97, 1.37). Conclusion In conclusion, this study did not find evidence for associations between TBI and anger proneness in this older population. Further research regarding relationships between anger proneness and TBI may not be warranted in older populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".