Intracranial Abnormalities Are Associated With Fewer Self-Reported Symptoms in Military Service Members Following Moderate-to-Severe Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: This study examined the relationship between intracranial abnormalities (ICAs) and self-reported neurobehavioral and posttraumatic stress (PTS) symptoms in members of the military with moderate-to-severe traumatic brain injury (msTBI). METHOD: Participants included 539 members of the US military with nonpenetrating msTBI. Self-reported neurobehavioral and PTS symptoms were assessed using the Neurobehavioral Symptom Inventory and the PTSD Checklist-Civilian Version. ICAs were categorized as present/absent (by subtype) based upon medical record review. Spearman rank-order correlations and stepwise multiple regression analyses examined univariate and combined predictive relationships between ICAs and self-reported symptoms. RESULTS: The presence of any ICA was associated with reduced self-reported neurobehavioral and PTS symptoms. ICA-associated reductions were largest for PTS, followed by affective and cognitive neurobehavioral symptoms, and relatively weak for somatic/sensory and vestibular symptoms. Effects of different types of ICAs were comparable. Greater time since injury was related to greater symptom report, whereas duration of loss of consciousness and posttraumatic amnesia were not consistently related to self-reported symptoms. CONCLUSIONS: Results suggest that ICAs are associated with suppression of reported PTS and neurobehavioral symptoms-potentially via reduction in self-awareness. These findings support comprehensive, objective evaluation to identify impairments in self-awareness and functioning in msTBI patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".