microRNA and Overcoming the Challenges of Their Use in the Diagnosis of Endometriosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it