A holistic biopsychosocial management approach for cis-gender males with chronic pelvic pain syndrome attending sexual health services: a retrospective case review
Bibliographic record
Abstract
OBJECTIVES: Chronic pelvic pain syndrome (CPPS) in men is a condition associated with significant morbidity which is typically managed in sexual health services. We introduced a modified biopsychosocial approach for managing CPPS in men, reducing use of antibiotics and evaluated its application in a retrospective case review. METHODS: Patients attended for a full consultation covering symptomology, onset and social history. Examination included urethral smear and assessment of pelvic floor tension and pain. A focus on pelvic floor relaxation was the mainstay of management with pelvic floor physiotherapy if required. Prescribing of antibiotics being discontinued if no evidence of urethritis at first consultation. The main outcome was change in the National Institute of Health Chronic Prostatitis Symptom Index (NIH-CPSI) score (which patients completed at each attendance); significant clinical improvement was defined as a NIH-CPSI score reduction of >25% and/or ≥6 points. RESULTS: Among 77 consecutive patients diagnosed with CPPS between April 2017 and December 2018, the mean NIH-CPSI score at the initial visit was 24.1 (11-42). Antibiotics were prescribed to 38/77 (49.4%) and alpha-blockers to 58/77 (75.3%). Overall, 50 (64.9%) patients with a mean initial NIH-CPSI score of 25.4 (11-42) re-attended a CPPS clinic. Among these, the average NIH-CPSI score at the final CPPS clinic appointment declined to 15.9 (0-39) (p<0.001); 34/50 (68%) men experienced significant clinical improvement. Men who attended only one CPPS clinic compared with those who reattended had a shorter duration of symptoms (18 (1-60) vs 36 (1-240) months; p=0.038), a lower initial NIH-CPSI score (21.7 (11-34) vs 25.4 (11-44); p=0.021), but had attended a similar number of clinics prior to referral (2.9 (0-6) vs 3.2 (0-8); p=0.62). CONCLUSIONS: The biopsychosocial approach significantly reduced the NIH-CPSI score in those who re-attended, with 68% of patients having a significant clinical improvement. The first follow-up consultation at 6 weeks is now undertaken by telephone for many patients, if clinically appropriate.
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 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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".