Bibliographic record
Abstract
OBJECTIVE: To present a practical approach to the symptom complex called chronic pelvic pain (CPP). Chronic pelvic pain is defined as nonmenstrual pain lasting 6 months or more that is severe enough to cause functional disability or require medical or surgical treatment. SOURCES OF INFORMATION: MEDLINE, EMBASE, and the Cochrane Database of Systematic Reviews were searched from January 1996 to December 2004. MAIN MESSAGE: While the source of pain in CPP can be gynecologic, urologic, gastrointestinal, musculoskeletal, or psychoneurologic, 4 conditions account for most CPP: endometriosis, adhesions, interstitial cystitis, and irritable bowel syndrome. More than one source of pain can be found in the same patient. Management involves treating the underlying condition, the pain itself, or both. Nonnarcotic analgesics are first-line therapy for pain relief; hormonal therapies are beneficial if the pain has a cyclical component. A multidisciplinary approach addressing environmental factors and incorporating medical management with physiotherapy, psychotherapy, and dietary modifications works best. CONCLUSION: Although caring for patients with CPP can be challenging and frustrating, family physicians are in an ideal position to manage and coordinate their care.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".