Algorithms Used in Ovarian Cancer Detection: A Minireview on Current and Future Applications
Bibliographic record
Abstract
BACKGROUND: Ovarian cancer is the 5th most common cause of cancer death among women in the US. Currently, there is no screening algorithm for asymptomatic women that has been shown to lower mortality rates. Screening is currently not recommended and has been shown to increase harm. Epithelial ovarian cancer (EOC) detection is reviewed, with a focus on high-grade serous, clear-cell, and endometrioid histotypes. CONTENT: A review of current literature surrounding tools used in detection of ovarian cancer will be presented. CA 125, HE4, risk of ovarian cancer algorithm (ROCA), risk of malignancy algorithm (ROMA), risk of malignancy (RMI), OVA1, and future potential biomarkers are reviewed. SUMMARY: Screening and early identification of EOC is currently managed as a single disease entity. However, recent evidence has shown ovarian cancer varies with relation to cellular origin, pathogenesis, molecular alterations, and prognosis, depending on histotype. There is a clear need for future studies identifying histotype-specific preclinical tumor markers to aid in detection and improvement of survival rates.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".