Pan-Asia adapted ESMO Clinical Practice Guideline for the management of patients with newly diagnosed and relapsed epithelial ovarian cancer
Bibliographic record
Abstract
The European Society for Medical Oncology (ESMO) Clinical Practice Guideline for the diagnosis, treatment and follow-up of patients with newly diagnosed and relapsed epithelial ovarian cancer (EOC), published in 2023, was adapted in July 2024, according to established standard methodology, to produce the Pan-Asian adapted ESMO consensus guideline for the management of Asian patients with EOC. The adapted guideline presented in this manuscript represents the consensus opinions reached by a panel of Asian experts in the treatment of patients with EOC representing the oncological societies of China, Indonesia, India, Japan, Korea, Malaysia, the Philippines, Singapore, Taiwan and Thailand, coordinated by ESMO and the Indian Society of Medical and Pediatric Oncology. Voting was based on scientific evidence and was independent of current treatment practices, drug access restrictions and reimbursement decisions in the represented countries. Drug access and reimbursement across Asia are discussed separately in the manuscript. The Pan-Asian consensus aims to guide the optimisation and harmonisation of management of patients with EOC in Asia, drawing on the evidence provided by both Western and Asian trials. Attention is drawn to the disparity in the drug approvals and reimbursement strategies between countries.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".