Measurement of pain and anthropometric parameters in women with chronic pelvic pain
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: To analyse anthropometric parameters, clinical pain and experimental pain in women with chronic pelvic pain (CPP). METHODS: Ninety-one women with a clinical diagnosis of CPP, mean age of 40.03 ± 9.97 years, submitted to anthropometric evaluation based on body mass index (BMI) and percent body fat (%BF) using bioimpedance body composition monitor; pain intensity was determined by visual analogue scale (VAS), numerical categorical scale (NCS) and McGill Pain Questionnaire; experimental pain was determined by transcutaneous electrical nerve stimulation (TENS), and anxiety and depression symptoms were determined by the Hospital Anxiety and Depression scale. RESULTS: A total of 54.8% of the women showed %BF >32 risk of disease associated with obesity. Regarding the anthropometric data, a statistically significant difference was observed between groups for both BMI and %BF (P<0.0001). In the analysis of pain intensity by the VAS, NCS and total McGill, there was no significant difference between the groups, and experimental pain by TENS revealed significant difference only between the normal weight and overweight groups (P=0.0154). The results of anxiety symptoms were above the cut-off point in all groups, with no significant difference between them (P=0.3710). The depression symptoms were below the cut-off point in the normal weight group and above the cut-off point in the overweight and obese groups, 9.469(4.501) and 9.741(4.848), respectively, with no significant difference between them (P=0.6476). CONCLUSION: Evaluation of anthropometric parameters and pain measurements can be applied in clinical practice, making a contribution to the diagnosis and influencing the choice of a more effective treatment for women with CPP.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".