Factors Influencing Postconcussion and Posttraumatic Stress Symptom Reporting Following Military-Related Concurrent Polytrauma and Traumatic Brain Injury
Bibliographic record
Abstract
The purpose of this study was to identify factors that are predictive of, or associated with, high endorsement of postconcussion and posttraumatic stress symptoms following military-related traumatic brain injury (TBI). Participants were 1,600 U.S. service members (age: M = 27.1, SD = 7.1; 95.4% male) who had sustained a mild-to-moderate TBI and who had been evaluated by the Defense and Veterans Brain Injury Center at one of six military medical centers. Twenty-two factors were examined that included demographic, injury circumstances/severity, treatment/evaluation, and psychological/physical variables. Four factors were statistically and meaningfully associated with clinically elevated postconcussion symptoms: (i) low bodily injury severity, (ii) posttraumatic stress, (iii) depression, and (iv) military operation where wounded (p < .001, 43.2% variance). The combination of depression and posttraumatic stress symptoms accounted for the vast majority of unique variance (41.5%) and were strongly associated with, and predictive of, clinically elevated postconcussion symptoms [range: odds ratios (OR) = 4.24-7.75; relative risk (RR) = 2.28-2.51]. Five factors were statistically and meaningfully associated with clinically elevated posttraumatic stress symptoms: (i) low bodily injury severity, (ii) depression, (iii) a longer time from injury to evaluation, (iv) military operation where wounded, and (v) current auditory deficits (p < .001; 65.6% variance accounted for). Depression alone accounted for the vast majority of unique variance (60.0%) and was strongly associated with, and predictive of, clinically elevated posttraumatic stress symptoms (OR = 38.78; RR = 4.63). There was a very clear, strong, and clinically meaningful association between depression, posttraumatic stress, and postconcussion symptoms in this sample. Brain injury severity, however, was not associated with symptom reporting following TBI.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".