Low-grade serous ovarian cancer: expert consensus report on the state of the science
Bibliographic record
Abstract
Compared with high-grade serous carcinoma, low-grade serous carcinoma of the ovary or peritoneum is a less frequent epithelial ovarian cancer type that is poorly sensitive to chemotherapy and affects younger women, many of whom endure years of ineffective treatments and poor quality of life. The pathogenesis of this disease and its management remain incompletely understood. However, recent advances in the molecular characterization of the disease and identification of novel targeted therapies with activity in low-grade serous carcinoma offer the promise of improved outcomes. To update clinicians regarding recent scientific and clinical trial advancements and discuss unanswered questions related to low-grade serous carcinoma diagnosis and treatment, a panel of experts convened for a workshop in October 2022 to develop a consensus document addressing pathology, translational research, epidemiology and risk, clinical management, and ongoing research. In addition, the patient perspective was discussed. The recommendations developed by this expert panel-presented in this consensus document-will guide practitioners in all settings regarding the clinical management of women with low-grade serous carcinoma and discuss future opportunities to improve research and patient care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.108 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".