From Roots to Canopy: Understanding Chronic Pelvic Pain Through a Tree-Inspired Model
Bibliographic record
Abstract
INTRODUCTION AND HYPOTHESIS: Chronic pelvic pain affects approximately 25% of women. Despite its prevalence, health care providers often find it challenging and may feel underprepared to offer effective care. METHODS: Using the metaphor of a tree provides a simplified, systematic approach to better understanding and thus managing this condition. The tree model unites both the biopsychosocial and neuroinflammatory models of chronic pain. In this metaphor, the tree's roots represent nociceptive inputs, the trunk represents nociplastic changes, and the canopy signifies psychosocial factors. The bark symbolizes protective behaviors adopted by the individual, whereas the sap represents the bidirectional nature of pain messaging. RESULTS: This metaphor provides a relatable visual framework for understanding chronic, persistent pelvic pain and serves as a foundation for history taking, physical examination, and care planning. The tree metaphor can help patients understand their pain and serve as a starting point for discussing treatment options. CONCLUSION: It helps to deconstruct the complexity of chronic, persistent pelvic pain into manageable components, offering a practical tool for both individuals with chronic pelvic pain and providers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".