Cumulative Blast Exposure During a Military Career Negatively Impacts Recovery from Traumatic Brain Injury
Bibliographic record
Abstract
Sub-concussive injuries have emerged as an important factor in the long-term brain health of athletes and military personnel. The objective of this study was to explore the relationship between service member and veterans (SMVs) lifetime blast exposure and recovery from a traumatic brain injury (TBI). A total of 558 SMVs with a history of TBI were examined. Lifetime blast exposure (LBE) was based on self-report (M = 79.4, standard deviation = 392.6; range = 0-7500) categorized into three groups: Blast Naive ( n = 121), Low LBE ( n = 223; LBE range 1-9), and High LBE ( n = 214; LBE >10). Dependent variables were the Neurobehavioral Symptom Inventory (NSI) and Post-traumatic Stress Disorder Checklist-Civilian (PCL-C) and the Traumatic Brain Injury Quality of Life (TBI-QOL). Analyses controlled for demographic factors (age, gender, and race) as well as TBI factors (months since index TBI, index TBI severity, and total number lifetime TBIs). The Blast Naive group had significantly lower NSI and PCL-C scores compared with the Low LBE group and High LBE group, with small to medium effect sizes. On the TBI-QOL, the Blast Naïve group had better quality life on 10 of the 14 scales examined. The Low LBE did not differ from the High LBE group on the PCL-C, NSI, or TBI-QOL. Blast exposure over an SMV's career was associated with increased neurobehavioral and post-traumatic stress symptoms following a TBI. The influence of psychological trauma associated with blasts may be an important factor influencing symptoms as well as the accuracy of self-reported estimates of LBE.
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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 | 0.003 |
| 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) | 0.002 | 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".