Correlations between Extent and Spread of Adenomyosis and Clinical Symptoms
Bibliographic record
Abstract
OBJECTIVE: To correlate the histopathology of adenomyosis particularly the depth and spread of adenomyosis and symptomatology. STUDY DESIGN: Medical records of 94 patients who had undergone a hysterectomy and who were found to have adenomyosis on histopathologic examination were reviewed and histopathologic slides were reexamined. The symptoms were correlated with the presence of adenomyosis, the depth of penetration, and the spread of adenomyosis foci. RESULTS: Specimens were stratified according to the degree of adenomyosis penetration into 4 groups: group A consisted of specimens with adenomyosis penetration into the myometrium of up to 25%; group B, 26-50%; group C, 51-75%, and group D, >75%. There was a significant correlation between the depth of penetration and the number of adenomyosis foci (r = 0.3446; p = 0.0001). Hemosiderin deposition was found mainly in the specimens with penetration of >75%. The symptoms did not correlate with the degree of penetration (r = 0.088; p NS). However, the spread of adenomyosis correlated significantly with pelvic pain (r = 0.80, p = 0.02), and with dysmenorrhea (r = 0.81, p = 0.01), but not with menorrhagia or dyspareunia. CONCLUSIONS: Hemosiderin deposition and adenomyosis foci are found predominantly in specimens with deep adenomyosis penetration. It suggests that the deeper the penetration, the more extensive the adenomyosis. Symptoms of adenomyosis do not correlate with the depth of penetration, but there is a correlation between the spread of adenomyosis and pelvic pain, and dysmenorrhea.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".