Bedside Testing for Chronic Pelvic Pain: Discriminating Visceral from Somatic Pain
Bibliographic record
Abstract
Objectives. This study was done to evaluate three bedside tests in discriminating visceral pain from somatic pain among women with chronic pelvic pain. Study Design. The study was an exploratory cross-sectional evaluation of 81 women with chronic pelvic pain of 6 or more months' duration. Tests included abdominal cutaneous allodynia (aCA), perineal cutaneous allodynia (pCA), abdominal and perineal myofascial trigger points (aMFTP) and (pMFTP), and reduced pain thresholds (RPTs). Results. Eighty-one women were recruited, and all women provided informed consent. There were 62 women with apparent visceral pain and 19 with apparent somatic sources of pain. The positive predictive values for pelvic visceral disease were aCA-93%, pCA-91%, aMFTP-93%, pMFTP-81%, and RPT-79%. The likelihood ratio (+) and 95% C.I. for the detection of visceral sources of pain were aCA-4.19 (1.46, 12.0), pCA-2.91 (1.19, 7.11), aMTRP-4.19 (1.46, 12.0), pMFTP-1.35 (0.86, 2.13), and RPT-1.14 (0.85, 1.52), respectively. Conclusions. Tests of cutaneous allodynia, myofascial trigger points, and reduced pain thresholds are easily applied and well tolerated. The tests for cutaneous allodynia appear to have the greatest likelihood of identifying a visceral source of pain compared to somatic sources of pain.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".