Influence of the Severity and Location of Bodily Injuries on Post-Concussive and Combat Stress Symptom Reporting after Military-Related Concurrent Mild Traumatic Brain Injuries and Polytrauma
Bibliographic record
Abstract
Traumatic brain injuries (TBI) sustained in combat frequently co-occur with significant bodily injuries. Intuitively, more extensive bodily injuries might be associated with increased symptom reporting. In 2012, however, French et al. demonstrated an inverse relation between bodily injury severity and symptom reporting. This study expands on that work by examining the influence of location and severity of bodily injuries on symptom reporting after mild TBI. Participants were 579 US military service members who sustained an uncomplicated mild TBI with concurrent bodily injuries and who were evaluated at two military medical centers. Bodily injury severity was quantified using a modified Injury Severity Score (ISSmod). Participants completed the Neurobehavioral Symptom Inventory (NSI) and the Posttraumatic Stress Disorder Checklist (PCL-C), on average, 2.5 months post-injury. There was a significant negative association between ISSmod scores and NSI (r=-0.267, p<0.001) and PCL-C (r=-0.273, p<0.001) total scores. Using linear regression to examine the relation between symptom reporting and injury severity across the six ISS body regions, three body regions were significant predictors of the NSI total score (face; p<0.001; abdomen; p=0.003; extremities; p<0.001) and accounted for 9.3% of the variance (p<0.001). For the PCL-C, two body regions were significant predictors of the PCL-C total score (face; p<0.001; extremities; p<0.001) and accounted for 10.5% of the variance. There was an inverse relation between bodily injury severity and symptom reporting in this sample. Hypothesized explanations include underreporting of symptoms, increased peer support, disruption of fear conditioning because of acute morphine use, or delayed expression of symptoms.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".