microRNA and Overcoming the Challenges of Their Use in the Diagnosis of Endometriosis
Bibliographic record
Abstract
Endometriosis is a common estrogen dependent and inflammatory disease affecting approximately 176 million women worldwide. Currently, the time between onset of symptoms and a definitive diagnosis has been reported by several international studies to range from 6 to 12 years. Presently, laparoscopic surgery followed by histopathological confirmation of lesions remains the gold standard for diagnosis. In part because of cost and invasiveness, current trends favor reduced laparoscopic surgeries in preference of the non-surgical diagnosis of endometriosis. However, the search for a clinical marker or markers of endometriosis that provide equal or similar sensitivity and specificity to laparoscopy has remained elusive. Thus, the search for a diagnostic test for the diagnosis of endometriosis continues to be a high priority research and clinical issue. Recent studies have reported favorable results with microRNA; however, lack of replication and absence of validation suggest that circulating miRNA may not be reliable for clinical use. Use of different screening platforms together with divergent methods may account for some of the lack or reproducibility in the literature. Herein we critically assess the recent literature and explore sources for discrepant findings. We suggest that prospective studies using validated reference miRNA to normalize results together with improved study design may yet reveal a suitable diagnostic marker or panel of markers for the diagnosis 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".