Ovarian cancer risk factors by tumor dominance, a surrogate for cell of origin
Bibliographic record
Abstract
Differentiating ovarian tumors based on developmental pathway may further enhance our understanding of the disease. Traditionally, ovarian cancers were thought to arise from the ovarian surface epithelium; however, recent evidence suggests that some tumors originate in the fallopian tube. We classified cases in a population-based case-control study (New England Case-Control [NECC] Study) and two cohort studies (Nurses' Health Study [NHS]/Nurses' Health Study II [NHSII]) by tumor dominance, a proxy for tissue of origin. Dominant tumors (likely ovarian origin) are restricted to one ovary or are at least twice as large on one ovary compared to the other. Ovarian cancer risk factors were evaluated in relation to dominant and nondominant tumors (likely tubal origin) using polytomous logistic regression (NECC) or competing risks Cox models (NHS/NHSII). Results were combined using random-effects meta-analyses. Among 1,771 invasive epithelial ovarian cancer cases, we observed 1,089 tumors with a dominant mass and 682 with no dominant mass. Dominant tumors were more likely to be mucinous, endometrioid or clear cell, whereas nondominant tumors were more likely to be serous. Tubal ligation, two or more births, endometriosis and age were more strongly associated with dominant tumors (rate ratio [RR] = 0.60, 0.83, 1.58 and 1.37, respectively) than nondominant tumors (RR = 1.03, 0.93, 0.84 and 1.14, respectively; p-difference = 0.0001, 0.01, 0.0003 and 0.01, respectively). These data suggest that risk factors for tumors putatively arising from ovarian versus fallopian tube sites may differ; in particular, reproductive factors may be more important for ovarian-derived tumors. As this is the first study to evaluate ovarian cancer risk factors by tumor dominance, these results need to be validated by other studies.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.013 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".