Epithelial ovarian cancer (EOC) without macroscopic residual tumour: Long-term, population-based outcomes
Bibliographic record
Abstract
5053 Background: Outcomes in “early” EOC have been uncertain, given inconsistent definitions, low incidence, fair prognosis, and few trials (of potentially unrepresentative populations). For 2 decades multi-modal treatment policies have been advocated for women with EOC in our province. All new pts were stratified into 4 risk groups using FIGO stage (S), grade (gr) and post-operative residual (invisible = R-, visible = R+): 3 groups are R- (Low, Moderate, High - see table for definitions); the 4th comprises the R+ and stage IV (Extreme). Methods: In the 17y including 1984 through 2000, 3501 pts were recorded to have “ovarian cancer”. On review, 82% (2886) of these were found to have EOC, 89% (2558) being invasive. The 1659 E pts are not subjects of this analysis. The records of the 895 R- pts were reviewed in detail. L pts normally received surgery alone. M and H pts were offered platinum-based chemotherapy (CT) and abdomino-pelvic radiotherapy (APR wasn't used in H pts from 1989 - 1994). Results:When CT and APR were advised, acceptance was usual, 97 and 90% respectively. The completion rate of APR was 89%, with 34% mild-moderate late diarrhea/colic and 3.5% needing surgery. Death from M and H EOC continues to be seen more than15y from diagnosis. Conclusions: This risk classification is useful; further analysis may allow refinement. With a durable disease-specific survival (DSS) of 94%, surgery alone is confirmed as sufficient for L; better therapy is needed for others as ∼1/3 and 1/2 of M and H pts respectively die of EOC by 15y. Ongoing analysis may indicate if APR has a positive therapeutic ratio. Author Disclosure Employment or Leadership Consultant or Advisory Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration Lerner David Littenberg Krumholz & Mentlik
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".