Enzian classification: does it correlate with clinical symptoms and the <scp>rASRM</scp> score?
Bibliographic record
Abstract
OBJECTIVE: To assess the extent to which the Enzian classification correlates with the revised American Society for Reproductive Medicine (rASRM) score and clinical symptoms in women with deeply infiltrating endometriosis. DESIGN: Retrospective cohort study (Canadian Task Force classification II-2). SETTING: Endometriosis competence center specializing in minimally invasive surgery. PATIENTS: Between 1 January 2009 and 31 December 2011, a total of 194 women underwent surgery due to deeply infiltrating endometriosis. After histological confirmation, they were classified using the rASRM and Enzian systems. Clinical symptoms were recorded preoperatively. INTERVENTIONS: Operative laparoscopy to treat endometriosis. MAIN OUTCOME MEASURES AND RESULTS: A clear correlation was seen between grades of severity in the rASRM score and the Enzian classification (p < 0.001). In addition, the rASRM severity grade and clinical symptoms correlated with the locations in the Enzian classification in relation to deeply infiltrating endometriosis. Pain and dysmenorrhea correlated strongly (p = 0.002, p < 0.001) with the severity grade in the Enzian classification. CONCLUSIONS: Deeply infiltrating endometriosis is well characterized using the Enzian classification as a supplement to the rASRM score. There is also a clear correlation between the rASRM and Enzian classifications, because of the way in which the disease crosses morphological boundaries. The locations in the Enzian classification correlate partially with clinical symptoms, and the classification's severity grades correlate substantially with pain and dysmenorrhea. In view of these clinical results, use of the Enzian classification can be recommended as a supplement to the rASRM score for detailed description of endometriosis.
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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 | 0.001 |
| 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) | 0.002 | 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".