Military Sexual Trauma’s Association with Lower Urinary Tract Symptoms (LUTS) and Fecal Incontinence (FI) Among U.S. Female Veterans
Bibliographic record
Abstract
IMPORTANCE: Military Sexual Trauma (MST) affects a large number of female veterans and is associated with various adverse physical and mental health conditions. Sexual trauma can lead to pelvic floor dysfunction, contributing to lower urinary tract symptoms (LUTS), a common urological concern, and fecal incontinence (FI). LUTS and FI may have a higher prevalence among female veterans with MST. OBJECTIVES: This study aimed to evaluate the prevalence and treatment of LUTS/FI among female veterans with a history of MST compared to those without. STUDY DESIGN: A retrospective cohort analysis was conducted using data from the Veterans Health Administration's (VHA) Corporate Data Warehouse. Baseline demographic data, International Classification of Diseases (ICD-9) codes, and medication use were analyzed, with logistic regression models controlling for confounders. RESULTS: Of the 416,137 female veterans analyzed, 103,877 (25%) reported a history of MST. Veterans with MST were more likely to be diagnosed with LUTS and FI, including a 22% (aOR 1.215; 95% CI 1.133, 1.302) increase in voiding issues and 17% (aOR 1.163; 95% CI 1.132, 1.194) increase in storage difficulties; 26% (aOR 1.260; 95% CI 1.136, 1.397) increase in interstitial cystitis/bladder pain syndrome (IC/BPS), and 34% (aOR 1.338; 95% CI 1.224,1.462) increase in FI. MST was associated with increased odds of undergoing diagnostic procedures for LUTS, such as cystoscopy (aOR 1.221; 95% CI 1.159, 1.287) and urodynamics (aOR 1.241; 95% CI 1.158,1.331). Veterans with MST were 15% more likely to receive pharmacological treatment for overactive bladder (aOR 1.152; 95% CI 1.122, 1.182). CONCLUSIONS: Female veterans with MST have a higher prevalence of LUTS and FI and are more likely to undergo diagnostic and therapeutic interventions.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".