Differences in Tumor Type in Low-stage Versus High-stage Ovarian Carcinomas
Bibliographic record
Abstract
Although there are recognized differences in the type of ovarian carcinomas between those tumors diagnosed at low versus high stage, there is a lack of data on stage distribution of ovarian carcinomas diagnosed according to the current histopathologic criteria from large population-based cohorts. We reviewed full slide sets of 1009 cases of 2555 patients diagnosed with ovarian carcinoma that were referred to the British Columbia Cancer Agency over a 16-year period (1984 to 2000). On the basis of the reviewed cases we extrapolated the distribution of tumor type in low-stage (I/II) and high-stage (III/IV) tumors. We then compared the frequencies with those seen in a large hospital practice. The overall frequency of tumor types was as follows: high-grade serous-68.1%, clear-cell-12.2%, endometrioid-11.3%, mucinous-3.4%, low-grade serous-3.4%, rare types-1.6%. High-grade serous carcinomas accounted for 35.5% of stage I/II tumors and 87.7% of stage III/IV tumors. In contrast, clear-cell (26.2% vs. 4.5%), endometrioid (26.6% vs. 2.5%), and mucinous (7.5% vs. 1.2%) carcinomas were relatively more common among the low-stage versus high-stage tumors. This distribution was found to be very similar in 410 consecutive cases from the Washington Hospital Center. The distribution of ovarian carcinoma types differs significantly in patients with low-stage versus high-stage ovarian carcinoma when contemporary diagnostic criteria are used, with consistent results seen in 2 independent case series. These findings reflect important biological differences in the behavior of the major tumor types, with important clinical implications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".