Posttraumatic Stress Disorder is a Stronger Predictor of Long‐Term Neurobehavioral Outcomes Than Traumatic Brain Injury Severity
Bibliographic record
Abstract
Concurrent posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) is common in military populations. The purpose of this study was to examine long-term neurobehavioral outcomes in service members and veterans (SMVs) with versus without PTSD symptoms following TBI of all severities. Participants were 536 SMVs prospectively enrolled from three military medical treatment facilities who were recruited into three experimental groups: TBI, injured controls (IC), and noninjured controls (NIC). Participants completed the PTSD Checklist, Neurobehavioral Symptom Inventory, and the TBI-Quality of Life (TBI-QOL) and were divided into six subgroups based on the three experimental categories, two PTSD categories (i.e., present vs. absent), and two broad TBI severity categories (unMTBI, which included uncomplicated mild TBI; and smcTBI, which included severe TBI, moderate TBI, and complicated mild TBI): (a) NIC/PTSD-absent, (b) IC/PTSD-absent, (c) unMTBI/PTSD-absent, (d) unMTBI/PTSD-present, (e) smcTBI/PTSD-absent, and (f) smcTBI/PTSD-present. There were significant main effects across the six groups for all TBI-QOL measures, ps < .001. Select pairwise comparisons revealed significantly lower scores, p < .001, on all TBI-QOL measures in the PTSD-present groups when compared to the PTSD-absent groups within the same TBI severity classification, ds = 0.90-2.11. In contrast, when controlling for PTSD, there were no significant differences among the TBI severity groups for any TBI-QOL measures. These results provide support for the strong influence of PTSD but not TBI severity on neurobehavioral outcomes following TBI. Concurrent PTSD and TBI of all severities should be considered a risk factor for poor long-term neurobehavioral outcomes that require ongoing monitoring.
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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 | 0.002 |
| 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 | 0.001 |
| 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".