Do Vulvodynia TCM Patterns Differ by Pain Types? Beginning Evidence Supporting the Concept
Bibliographic record
Abstract
INTRODUCTION: Vulvodynia affects a maximum of 14 million U.S. women; however, it has not been adequately characterized. Traditional Chinese Medicine (TCM) offers pattern diagnoses that may be considered vulvodynia phenotypes and may guide the development of more targeted treatments. OBJECTIVES: In women with vulvodynia, to explore relationships between the TCM patterns and pain. DESIGN/METHODS: In an exploratory study, 36 women diagnosed with vulvodynia had a TCM assessment and completed the Short Form McGill Pain Questionnaire (SF-MPQ). RESULTS: All 36 women were diagnosed with one of the two TCM patterns (excess heat [n = 28] or excess cold [n = 8]). Although not statistically significant, (1) the excess heat pattern group had a higher mean sensory score (14.4 ± 6.0) and mean affective pain score (4.1 ± 2.8) (more pain) compared with the mean sensory score (13.3 ± 5.9) and mean affective score (3.3 ± 1.8) of the excess cold pattern group; (2) there was a higher mean score for neuropathic sensory descriptors in the excess heat pattern group (1.55 ± .58) compared with the excess cold pattern group (1.16 ± 0.72); and (3) there was a higher mean score for nociceptive sensory descriptors in the excess cold pattern group (1.23 ± 0.45) compared with the excess heat pattern group (1.14 ± 0.62). The difference in the hot-burning mean score between the two TCM pattern groups was statistically significant (t [34] = 6.55, p < 0.0001). CONCLUSION: Intriguing trends were observed in the pain scores for the two TCM pattern groups. The possibility that TCM pattern groups have different types of pain (neuropathic vs. nociceptive) deserves further research in larger samples. If these exploratory findings are confirmed, the characterization of TCM patterns could lead to new treatments for vulvodynia.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".