Symptom and Anatomical Phenotypes Provide Insights Into Interactions of Prolapse Symptoms and Anatomy
Bibliographic record
Abstract
IMPORTANCE: Women pursue treatment to relieve symptoms, while surgeons repair anatomy, underlining the importance of the relationship between symptoms and anatomy. OBJECTIVE: We hypothesized different anatomical and symptom phenotypes associated with pelvic organ prolapse (POP). Our objective was to investigate prevalence of phenotypes to explore associations of symptoms with anatomical defects. METHODS: We defined 420 anatomical phenotypes from combinations of POP Quantification parameters and 128 symptom phenotypes from symptoms described by condition-specific questionnaires (Pelvic Floor Disorders Inventory, Short Form of the Personal Experience Questionnaire). We applied these to an anonymized database of 719 subjects with symptomatic pelvic floor disorders. Bar graphs were used to illustrate the distribution of anatomical and symptom phenotypes, as well as anatomical phenotypes of patients with specific symptoms. We then used biclustering analysis with the multiple latent block model, to identify patterns of clustered groups of subjects and features. RESULTS: The most common symptom phenotypes have multiple (3-5) symptoms. A third of the theoretical anatomical phenotypes existed in our cohort. Bar graphs for specific symptom composites demonstrated unique distributions of anatomical phenotypes suggesting associations between anatomy and symptoms. Biclustering converged on 2 subject clusters (C1, C2) and 8 feature clusters. Cluster 1 (68%) represented a younger subpopulation with lower stage POP, more stress urinary incontinence and sexual dysfunction (P < 0.001 all). Cluster 2 had more protrusion (P < 0.001) and obstructed voiding (P = 0.001). Features that clustered together, such as stress urinary incontinence and sexual dysfunction, may represent underlying relationships. CONCLUSIONS: We demonstrated a relationship between locations of anatomical POP and certain symptoms, which may generate new hypotheses and guide clinical decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".