Treatment of Recurrent Low-grade Serous Ovarian Cancer With MEK Inhibitors
Bibliographic record
Abstract
OBJECTIVE: Low-grade serous ovarian cancer (LGSC) represents 5% of all epithelial ovarian cancers. They are characterized by indolent growth and KRAS and BRAF mutations, differing from high-grade serous ovarian cancer both clinically and molecularly. LGSC has low response rates to traditional systemic therapies, including chemotherapy and hormonal therapy. The objective of this systematic review was to appraise the literature describing the efficacy of MEK inhibitors in the treatment of LGSC. METHODS: A comprehensive search was conducted of the following databases: Medline ALL, EMBASE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Web of Sciences, ClinicalTrials.gov, International Clinical Trials Registry Platform (ICFRP), and International Standard Randomized Controlled Trials Number (ISRCTN) Registry. All studies investigating MEKi in the treatment of LGSC in the adjuvant or recurrent setting for patients 18 years of age or older were included. All titles/abstracts were then screened by 2 independent reviewers (A.K. and C.C.). The full-text articles were then screened. All disagreements were resolved by a third independent reviewer (T.Z.). Two independent reviewers (A.K. and C.C.) extracted data from the studies deemed eligible for final review. RESULTS: A total of 2108 studies were identified in the initial search. Of these, a total of 4 studies met the eligibility criteria for systematic review. In these studies, 416 patients were treated with an MEKi alone. All patients included in the studies were being treated for LGSC in the recurrent setting. Varied results and efficacy of the MEKi were reported in each study. CONCLUSIONS: The results highlighted in this systematic review demonstrate varied responses to MEKi for recurrent LGSC. Further research is needed in this field comparing the efficacy to current therapies, as well as to further evaluate the safety and toxicity profile with long-term use of MEKi.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".