Additional file 2 of Quality of life and symptoms of pain in patients with endometriomas compared to those with other endometriosis lesions: a cross-sectional study
Bibliographic record
Abstract
Additional file 2: Table S1. Frequency and percentage of endometriosis subtypes in the study sample. Table S2. Number and size of endometriomas in participants with surgical diagnosis. Figure S1. Subgroup analyses of mean difference in superficial dyspareunia intensity of patients with endometrioma compared to those with other types of endometriosis regarding potential modification factors. Figure S2. Subgroup analyses of mean difference in chronic pelvic pain intensity of patients with endometrioma compared to those with other types of endometriosis regarding potential modification factors. Figure S3. Subgroup analyses of mean difference in deep dyspareunia intensity of patients with endometrioma compared to those with other types of endometriosis regarding potential modification factors. Figure S4. Subgroup analyses of mean difference in lower back pain intensity of patients with endometrioma compared to those with other types of endometriosis regarding potential modification factors. Figure S5. Subgroup analyses of mean difference in dysmenorrhea intensity of patients with endometrioma compared to those with other types of endometriosis regarding potential modification factors. Figure S6. Subgroup analyses of mean difference in dyschezia intensity of patients with endometrioma compared to those with other types of endometriosis regarding potential modification factors. Figure S7. Impaired quality of life and severe pain symptoms of patients with endometriomas compared to those with other lesions 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.519 | 0.020 |
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".