CLINICAL BIOMARKERS FOR THE NONINVASIVE DIAGNOSIS OF ENDOMETRIOSIS
Bibliographic record
Abstract
Endometriosis is a chronic estrogen-dependent gynecological disease where endometrial cells implant at inappropriate sites causing significant pelvic pain, decreased quality of life, and often infertility. It affects 10% of women of reproductive age, and there is no minimally invasive diagnostic test. Consequently the time to diagnosis, which occurs during laparoscopic surgery followed by pathological confirmation of disease, is prolonged and exceeds 11 years. During this time, the disease often worsens and women thus experience avoidable morbidity. Additionally, endometriosis is a financial burden on the healthcare system, with annual costs of $69.4 billion (U.S.) and $1.8 billion (Canada) in 2009. For these reasons, identifying a clinical marker remains a top priority. Although over 100 putative markers have been identified and reviewed, none have proven sufficiently accurate or reliable for disease diagnosis. For endometriosis to develop endometrial tissue must evade the immune system and adhere, implant, create new vasculature, and grow at ectopic locations. As such it is likely that abnormalities in many or all of these pathways are requisite for disease formation and progression. With this in mind, serum concentrations of eight putative biomarkers believed to be involved in varying pathogenic processes were compared between patients with both surgically and histologically confirmed presence (n=96) and absence (n=25) of endometriosis. Results showed there to be a significant elevation in two of these markers (glycodelin p<0.001, and zinc alpha 2-glycoprotein (ZAG) p=0.009 when hormonally untreated cases (n=57) were compared to controls. ROC analysis revealed glycodelin to have a sensitivity of 81.6% and specificity of 69.6% for disease diagnosis, while ZAG had a sensitivity of 46% and a specificity of 100%. Subsequent analysis revealed that if combined in panel, using both glycodelin and ZAG could result in a test with a sensitivity of 90%, and a specificity of 65%, giving greater accuracy for disease detection than either independently.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".