ESGO–ESMO–ESP consensus conference recommendations on ovarian cancer: pathology and molecular biology and early, advanced and recurrent disease
Bibliographic record
Abstract
The European Society of Gynaecological Oncology, the European Society for Medical Oncology (ESMO) and the European Society of Pathology held a consensus conference (CC) on ovarian cancer on 15-16 June 2022 in Valencia, Spain. The CC panel included 44 experts in the management of ovarian cancer and pathology, an ESMO scientific advisor and a methodologist. The aim was to discuss new or contentious topics and develop recommendations to improve and harmonise the management of patients with ovarian cancer. Eighteen questions were identified for discussion under four main topics: (i) pathology and molecular biology, (ii) early-stage disease and pelvic mass in pregnancy, (iii) advanced stage (including older/frail patients) and (iv) recurrent disease. The panel was divided into four working groups (WGs) to each address questions relating to one of the four topics outlined above, based on their expertise. Relevant scientific literature was reviewed in advance. Recommendations were developed by the WGs and then presented to the entire panel for further discussion and amendment before voting. This manuscript focuses on the recommendation statements that reached a consensus, their voting results and a summary of evidence supporting each recommendation.
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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 | 0.061 | 0.104 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.021 | 0.013 |
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".