Biopsychosocial predictors of suicide risk in patients with interstitial cystitis/bladder pain syndrome
Bibliographic record
Abstract
INTRODUCTION: The impact of interstitial cystitis/bladder pain syndrome (IC/BPS) is prevalent and severe. Studies examining the IC/BPS prevalence and predictors of suicide risk are limited by their lack of theoretically relevant suicide research variables. This research reports suicide risk prevalence and its biopsychosocial predictors for a community IC/BPS sample. METHODS: Self-identified female patients suffering from IC/BPS (n=813; 18-80 years, mean 46.60, standard deviation [SD] 14.10) recruited from online IC/BPS support groups completed measures of demographic, pain, symptoms, and psychosocial variables. Descriptive statistics, correlations, and multivariable logistic regressions examined prevalence, variable associations, and suicide risk prediction. RESULTS: Suicide risk prevalence was 38.1%. Suicide risk was associated with greater odds for exposure to suicide, psychache, hopelessness, and perceived burdensomeness to others. Further, examining suicide risk by levels of pain showed that exposure to suicide and hopelessness were consistent suicide risk predictors across pain levels; psychache for lower levels of pain, depression in moderate levels of pain, and perceived burdensomeness in moderate and severe pain levels. CONCLUSIONS: The high prevalence of suicide risk is alarming and signifies an imperative for recognizing this risk within the IC/BPS population. The identified psychosocial risk factors may be used in refining screening and treatment, and in directing future IC/BPS research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".