A critical appraisal of the circulating levels of differentially expressed microRNA in endometriosis
Bibliographic record
Abstract
Endometriosis is a common gynecological condition characterized by estrogen dependence, chronic pelvic pain, infertility, and diagnostic delay of between 5.4 and 12 years. Despite extensive study, no biomarker, either alone or in combination with other markers, has proven superior to laparoscopy for the diagnosis of endometriosis. Recent studies report that circulating levels of differentially expressed microRNA (miRNA) in women with endometriosis compared with controls are potential diagnostic tools. However, the lack of replication and absence of validated differential expression in novel study populations have led some to question the diagnostic value of miRNA. To elucidate potential reasons for the lack of replication of study results and explore future directions to enhance replicability of circulating miRNA results, we carried out an electronic search of the miRNA literature published between 2000 and 2020. Eighteen studies were identified in which 63 different miRNAs were differentially expressed in the circulation of women with endometriosis compared with controls. However, the differential expressions of only 14 miRNAs were duplicated in one or more studies. While individual miRNAs lacked diagnostic value, miRNA panels yielded sensitivity and specificity equal to or better than laparoscopy in five studies. Important differences in study design, sample processing, and analytical methods were identified rendering direct comparisons across studies problematic and could account for the lack of reproducibility of study results. We conclude that while the results of miRNA studies to date are encouraging, refinements to study design and analytical methods should enhance the reliability of circulating miRNA 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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".