Traumatic Brain Injury in a Community-Based Cohort of Homeless and Vulnerably Housed Individuals
Bibliographic record
Abstract
We characterized traumatic brain injury (TBI) and studied its associations with mental and physical health in a community cohort of homeless and vulnerably housed individuals. Detailed mental and physical health structured interviews, neuropsychological testing, and multimodal magnetic resonance imaging (MRI) were performed on 283 participants. Two TBI participant groups were defined for primary analyses: those with a self-reported history of TBI and those with MRI confirmation of TBI. By self-report, 174 participants (61.5%) reported a previous serious head or face injury (symptomatic or asymptomatic), with 100 (35.3%) experiencing symptoms consistent with TBI (any post-injury loss of consciousness, confusion, or memory loss). Persons self-reporting TBI had poorer current mental and physical health, more ongoing neurological symptoms, and a higher rate of mood disorders, compared to those with no TBI. The presence of a mood disorder, a TBI history, and an interaction between these factors contributed to lower mental health. There was evidence of TBI in 20 participants (6.9%) on clinical MRI sequences. These participants had globally lower cortical gray matter volumes and lower white matter fractional anisotropy (FA) values. Neurocognitive test scores positively correlated with both FA and cortical gray matter volumes in participants with MRI evidence of trauma. Previous TBI is associated with poorer mental and physical health in homeless and vulnerably housed individuals and interacts with mood disorders to exacerbate poor mental health. Focal traumatic lesions evident on MRI are associated with diffusely lower gray matter volumes and white matter integrity, which predict cognitive functioning.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".