Comprehensive endometriosis care: a modern multimodal approach for the treatment of pelvic pain and endometriosis
Bibliographic record
Abstract
Endometriosis is a prevalent gynecological disease, leading to chronic pain and inflammation, affecting 1 in 10 individuals presumed female at birth. The diagnostic journey is often arduous, marked by neglect of the right diagnosis and prolonged wait times, significantly compromising the quality of life among those affected. This review provides a nuanced exploration of endometriosis-associated pain management, encompassing medical, surgical, and holistic approaches, all guided by accurate and refined diagnostics. Our paramount goal is to empower physicians as key figures in confronting this intricate challenge with a patient-centric approach, ultimately aiming to improve treatment and quality of life. Acknowledging each patient's unique needs, we emphasize the importance of tailoring a spectrum of options informed by current literature and insights gleaned from our experience in a high-volume tertiary endometriosis center. It is imperative to recognize endometriosis as a complex and chronic disease, often occurring with co-morbid conditions and nuanced complexities, necessitating a long-term personalized multimodal approach for each case. In addition, incorporating principles such as patient autonomy, profound respect for diverse experiences, and practical education on treatment choices is pivotal in enhancing treatment outcomes and overall patient satisfaction.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".