Low-Grade Serous Carcinoma of the Ovary: The Current Status
Bibliographic record
Abstract
Low-grade serous carcinoma (LGSC) of the ovary is a rare histological subtype of epithelial ovarian carcinoma. It has distinct clinical behavior and a specific molecular profile. Compared with high-grade serous carcinoma, this tumor presents at a younger age, has an indolent course, and is associated with prolonged survival. LGSC can arise de novo or originate following a serous borderline tumor (SBT). Pathological differentiation between LGSC and other ovarian carcinoma histological subtypes is fundamental. Several factors might influence the overall outcome, such as the age at diagnosis, current smoking, elevated body mass index, mutational status, hormonal receptors' expression, and Ki-67 proliferation index. Surgery is the main treatment option in LGSC, and efforts must be maximized to achieve a microscopic residual in metastatic disease. Despite being relatively chemo-resistant, adjuvant platinum-based chemotherapy remains the standard of care in LGSC. Hormonal maintenance therapy after adjuvant chemotherapy results in improved outcomes. Treatment options for disease recurrence include secondary cytoreductive surgery, chemotherapy, hormonal therapy, targeted therapy, and clinical trials. Advancements in genomic studies and targeted therapies are expected to change the treatment landscape in LGSC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".