Use of “Repurposed” Drugs in the Treatment of Epithelial Ovarian Cancer
Bibliographic record
Abstract
Epithelial ovarian cancer has poor outcomes with standard therapy and limited options for treatment of recurrent disease. This systematic review summarizes the data on the clinical use of repurposed drugs. We searched for clinical studies using "repurposed" agents for the treatment of ovarian cancer in the following databases: PubMed, clinicaltrials.gov, Clinical Trial Registry of India, European Clinical Trials Registry, and Chinese Clinical Trial Registry. We excluded reviews, preclinical studies, and non-English language studies. We assessed the quality of included studies. The following agents/class of agents were included: statins, hydroxychloroquine, metformin, itraconazole, nonsteroidal anti-inflammatory drugs, vitamin D, proton pump inhibitors, beta-blockers, and sodium valproate. Only one randomized controlled trial investigated metformin, which found no benefit of metformin. However, this had a high risk of bias (no details of randomization). Among the observational studies, 70% were of high quality (Newcastle-Ottawa scale ≥7). Clinical benefit was seen for itraconazole, beta-blockers, metformin, statins, and proton pump inhibitors. Though multiple studies aim to repurpose agents in epithelial ovarian cancer, the most published literature is observational, and none are practice-changing. Given the solid preclinical data regarding the anticancer efficacy of these agents, well-designed clinical trials are urgently required.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| 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".