Influence of PTSD and MDD on somatic symptoms in treatment-seeking military members and Veterans
Bibliographic record
Abstract
Introduction: Using a treatment-seeking sample of military personnel and Veterans ( n = 736), the objectives were to determine the prevalence of somatic symptoms in the sample and investigate whether the mean severity of somatic symptoms differed between common probable psychiatric conditions and comorbidity. Methods: The Patient Health Questionnaire–15 was used to determine somatic symptom severity. One-way analyses of variance and Tukey post hoc tests determined whether the severity of somatic symptom categories (musculoskeletal pain, neurological, cardiovascular, gastrointestinal, sleep, and lethargy) and total somatic symptom severity differed significantly between groups. Results: Most participants (80%) reported moderate to high levels of somatic symptoms, and more than half the sample had probable comorbid post-traumatic stress disorder (PTSD) and major depressive disorder (MDD). Mean total somatic symptom severity for the comorbid PTSD–MDD group was high and differed significantly from that of the PTSD- and MDD-only groups (medium severity) and the group with neither condition (mild severity). Severity of most mean somatic symptom categories differed significantly between comorbid PTSD and MDD for all other groups. Discussion: Results suggest that the presentation of comorbid PTSD and MDD is more detrimental in terms of somatic symptom severity than that of either disorder separately. Although there were some differences in the severity of specific somatic symptom types between the PTSD-only and the MDD-only groups, overall severity did not differ. After diagnosis of a mental health condition, military personnel and Veterans should be screened for somatic 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 | 0.003 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".