Prospective Analysis of Premilitary Mental Health, Somatic Symptoms, and Postdeployment Postconcussive Symptoms
Bibliographic record
Abstract
OBJECTIVES: Many recent studies of service members returning from deployment have focused on the health impacts of mild traumatic brain injury (mTBI), including persistent postconcussive symptoms (PCS). However, cross-sectional study designs have made it difficult to understand the role of mental health in the etiology of persistent PCS. METHODS: Participants were 3319 military personnel (primarily men [90%] of 25-34 years [54%]) who had completed health surveys at basic training and after deployment, on average, 4.6 years later. Negative binomial regression was used to assess the association of PCS with demographic covariates, premilitary mental health and somatic symptoms, combat experiences and mTBI during deployment, in addition to postdeployment mental health and non-PCS somatic symptoms. RESULTS: Premilitary mental health and somatic symptoms predicted PCS even when adjusting for other variables, yielding an elevated incidence rate ratio (IRR) for posttraumatic stress disorder (PTSD; IRR = 1.23, 95% confidence interval [CI] = 1.06-1.41) and somatic symptoms (mild versus minimal somatic symptoms: IRR = 1.43, 95% CI = 1.31-1.55; moderate/severe versus minimal somatic symptoms: IRR = 1.69, 95% CI = 1.43-2.06), but not for depressive symptoms. When postdeployment mental health and somatic symptom measures were added to the model, the effect of premilitary somatic symptoms remained significant. CONCLUSIONS: Findings point to potential etiological contributions of premilitary characteristics, particularly a tendency to experience somatic symptoms and PTSD, as well as mTBI and combat experiences, to the development of PCS. PCS were also strongly related to concurrent postdeployment mental health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".