ACR Appropriateness Criteria© Ovarian Cancer Screening
Bibliographic record
Abstract
The majority of women with ovarian cancer have advanced stage disease at the time of diagnosis and a poor 5 year survival rate. Hence, screening has been investigated in the hopes of improving survival by diagnosing ovarian cancer at an earlier stage. Most screening methods thus far have included ultrasound and/or serum tumor markers. However, low prevalence of the disease, high false positive rate of current screening methods, and the probable rapid growth of most ovarian carcinomas from no defined precursor lesion, all contribute to difficulty in screening for ovarian cancer. While screening may be able to detect ovarian cancer at an earlier stage, adequate data is presently lacking on whether screening improves survival. The results of ongoing large clinical trials will be available in a few years and should provide critical information regarding the usefulness of screening. Pending results of those large clinical trials, screening is not currently recommended for women at average risk for ovarian cancer. Screening is most likely to be performed in women with an increased familial risk of ovarian cancer, but patients should be aware that even with this risk factor, there is currently insufficient evidence to know if screening is effective. New screening methods, including new or multiple serum markers and proteomics, are also being investigated.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.014 |
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".