Predictors of quality of life and pain in chronic prostatitis/chronic pelvic pain syndrome: findings from the National Institutes of Health Chronic Prostatitis Cohort Study
Bibliographic record
Abstract
OBJECTIVES: To examine the cross-sectional relationship of age, urinary and depressive symptoms and partner status on pain intensity and quality of life (QoL) in chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS). PATIENTS AND METHODS: In all, 463 men enrolled in the National Institutes of Health (NIH) Chronic Prostatitis Cohort Study from seven clinical centres (six in the USA and one in Canada) reported baseline screening symptoms using the NIH Chronic Prostatitis Symptom Index (CPSI). The CPSI provides scores for pain, urinary symptoms and QoL. In addition, a demographic profile, including age and partner (living with another) status, and a depressive symptom score were obtained. Regression modelling of QoL, adjusting for between-centre variability, examined the unique effects of age, partner status, urological symptoms, depressive symptoms and pain. RESULTS: Urinary scores, depressive symptoms and pain intensity scores significantly predicted QoL for patients with CP/CPPS (higher CPSI QoL scores indicated more impairment; median 8.0, range 0-12). On average, for every 1-point increase in urinary scores, there was a corresponding increase in QoL score of 0.118 points (P = 0.001); for every 1-point increase in pain intensity score, there was a corresponding increase in QoL score of 0.722 points (P < 0.001); and for every 1-point decrease in depressive symptoms (lower scores equal poorer mood), the QoL score increased (poorer quality of life) by 0.381 points (P < 0.001). Age and partner status did not significantly contribute to poorer QoL. Urinary scores and depressive symptoms were significant predictors (P < 0.001) of pain in patients with CP/CPPS. CONCLUSIONS: These data show that depressive symptoms and pain intensity significantly predict a poorer QoL in patients with CP/CPPS, and that these effects are independent of partner status, age and urinary status. In particular, pain intensity was the most robust predictor of a poorer QoL. Further data relating pain and psychological factors to CP/CPPS are highly recommended, to aid in determining specific factors for pain and its impact on QoL. These data are essential if empirically guided efforts to manage pain are to progress.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".