Estimating the Prevalence of Ovarian Cancer Symptoms in Women Aged 50 Years or Older: Problems and Possibilities
Bibliographic record
Abstract
Diagnostic testing is recommended in women with "ovarian cancer symptoms." However, these symptoms are nonspecific. The ongoing Diagnosing Ovarian Cancer Early (DOVE) Study in Montreal, Quebec, Canada, provides diagnostic testing to women aged 50 years or older with symptoms lasting for more than 2 weeks and less than 1 year. The prevalence of ovarian cancer in DOVE is 10 times that of large screening trials, prompting us to estimate the prevalence of these symptoms in this population. We sent a questionnaire to 3,000 randomly sampled women in 2014-2015. Overall, 833 women responded; 81.5% reported at least 1 symptom, and 59.7% reported at least 1 symptom within the duration window specified in DOVE. We explored whether such high prevalence resulted from low survey response by applying inverse probability weighting to correct the estimates. Older women and those from deprived areas were less likely to respond, but only age was associated with symptom reporting. Prevalence was similar in early and late responders. Inverse probability weighting had a minimal impact on estimates, suggesting little evidence of nonresponse bias. This is the first study investigating symptoms that have proven to identify a subset of women with a high prevalence of ovarian cancer. However, the high frequency of symptoms warrants further refinements before symptom-triggered diagnostic testing can be implemented.
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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 | 0.075 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".