A Prospective Study Consortium for the Discovery and Validation of Early Detection Markers for Ovarian Cancer – Baseline Findings for CA125
Bibliographic record
Abstract
PURPOSE: Epithelial ovarian cancer (EOC) is a lethal malignancy. Cancer antigen 125 (CA125), the "best" available marker for detecting EOC, has insufficient sensitivity and specificity for earlier-stage disease and is not a meaningful screening tool, motivating the search for further biomarkers. Cancer biomarker discovery is enhanced by "omics" technologies. Discovery studies for EOC biomarkers should be conducted in prediagnosis blood samples from prospective cohorts to maximize the likelihood of identifying markers that can detect disease before usual diagnosis and in earlier disease stage while reducing methodologic biases. EXPERIMENTAL DESIGN: Individual cohorts with prediagnosis blood samples have insufficient sample size for such studies. Thus, we established "Prospective Early Detection Consortium for Ovarian Cancer" ("PREDICT")-a collaboration of nine prospective studies-to assemble a sufficient number of EOC cases with blood samples collected ≤18 months before diagnosis plus controls. The 457 cases and 1,687 controls have circulating CA125 measured using a clinical assay. RESULTS: The discrimination capacity for single CA125 measurements in samples collected <6 months prior to diagnosis was high (AUC; PREDICT overall = 0.92; range across cohorts of nonpregnant individuals = 0.89-0.98) and declined with extended time between blood collection and diagnosis. Between-cohort variability in CA125 levels and predictive performance was observed. CONCLUSIONS: Ongoing investigations in PREDICT are evaluating the early detection potential of tumor-associated autoantibodies and miRNAs using CA125 as a benchmark. PREDICT is a well-characterized resource for identifying and validating detection markers for EOC that may then be used in multimodal screening as a complement to CA125 and combined with imaging.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".