The Gynecologic Surgeon's Approach to Evaluating a Patient With Suspected Endometriosis: A Systematic Review
Bibliographic record
Abstract
Background: The objective of this review is to assess the most recent literature recommendations for how to approach a patient presenting with chronic pelvic pain (CPP), when a high clinical suspicion of endometriosis exists, to prevent the clinical and economic burden associated with delayed diagnosis. Methods: An online review of PubMed and Europe PubMed Central was conducted with a final total of 11 articles being reviewed. The search was limited to the preoperative management of these patients, excluding literature focused on the effectiveness of medical vs. surgical management of the disease. There is no main outcome measure. Results: A thorough history of patient symptomatology and physical exam are paramount to a timely diagnosis of endometriosis. Additionally, the literature supports the use of sonogram as the first-line imaging modality for diagnosis; however, its utility is limited to detection of the two less common forms of the disease, endometrioma and deep peritoneal lesions, with less reliable prediction of superficial implants. If a high clinical suspicion exists for either endometrioma or deep infiltrating disease, magnetic resonance imaging can reliably demonstrate these findings. The gold standard method of laparoscopy for definitive diagnosis is not controversial; however, the literature suggests that proper, sequential evaluation by history, physical and imaging may aid in accurate and timely diagnosis. Delay in the diagnosis of endometriosis creates a significant burden on patient well-being as well as an economic burden on the healthcare system. Conclusion: Further studies assessing biomarkers of endometriosis and specific features of the disease in time are needed to better understand the etiology and pathogenesis and to subsequently decrease disease burden. J Clin Gynecol Obstet. 2021;10(2):40-45 doi: https://doi.org/10.14740/jcgo742
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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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".