Ovarian carcinoma diagnosis
Bibliographic record
Abstract
BACKGROUND: Ovarian carcinoma often is called the "silent killer" because the disease usually is not detected until an advanced stage. The authors' goal was to evaluate preoperative symptoms and factors that may contribute to delayed diagnosis for women with ovarian carcinoma. METHODS: A two-page survey was distributed to 1500 women who subscribe to CONVERSATIONS!, a newsletter about ovarian carcinoma. Because the survey could be copied and given to other patients, 1725 surveys were returned from women in 46 states and 4 Canadian provinces. RESULTS: The median age of the surveyed women was 52 years, and 70% had Stage III or IV disease (International Federation of Gynecology and Obstetrics). When asked about symptoms before the diagnosis of ovarian carcinoma, 95% reported symptoms, which were categorized as abdominal (77%), gastrointestinal (70%), pain (58%), constitutional (50%), urinary (34%), and pelvic (26%). Only 11% of women with Stage I/II and 3% with Stage III/IV reported no symptoms before their diagnosis. Women who ignored their symptoms were significantly more likely to be diagnosed with advanced disease compared with those who did not (P = 0.002). The time required for a health care provider to make the diagnosis was reported as less than 3 months by 55%, but greater than 6 months by 26% and greater than 1 year by 11%. Factors significantly associated with delay in diagnosis were omission of a pelvic exam at first visit; having a multitude of symptoms; being diagnosed initially with no problem, depression, stress, irritable bowel, or gastritis; not initially receiving an ultrasound, computed tomography, or CA 125 test; and younger age. The type of health care provider seen initially, insurance, and specific symptoms did not correlate with delayed diagnosis. CONCLUSIONS: This large national survey confirms that the majority of women with ovarian carcinoma are symptomatic and frequently have delays in diagnosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".