Efficacy and safety of PD-1/PD-L1 immune checkpoint inhibitors in the treatment of recurrent ovarian cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Recurrent ovarian cancer (OC) presents a significant therapeutic challenge with limited treatment success. Programmed cell death protein 1 (PD-1/PD-L1) immune checkpoint inhibitors have emerged as a potential treatment avenue, necessitating a systematic review and meta-analysis to evaluate their efficacy and safety. METHODS: Adhering to preferred reporting items for systematic reviews and meta-analyses guidelines, we conducted a comprehensive literature search across PubMed, Embase, Web of Science, and Cochrane Library, culminating in the inclusion of studies focusing on the treatment of recurrent OC with PD-1/PD-L1 inhibitors. Studies were evaluated using the Newcastle-Ottawa Scale and analyzed using fixed or random effects models depending on heterogeneity levels. RESULTS: Our search yielded 1215 articles, with 6 meeting the inclusion criteria for final analysis. Studies varied in size and reported median age, overall survival (OS), progression-free survival (PFS), and adverse events. The meta-analysis showed improved Objective Response Rates (ORR), Disease Control Rate (DCR), and PFS in patients treated with PD-1/PD-L1 inhibitors. The overall adverse event rate was 17.9%, indicating a need for careful patient selection and monitoring. No significant publication bias was detected, enhancing the reliability of our findings. CONCLUSIONS: PD-1/PD-L1 inhibitors offer a promising treatment option for recurrent OC, improving ORR, DCR, and PFS. However, the higher incidence of adverse events necessitates a cautious approach to their use. Future research should focus on long-term outcomes, biomarker identification, and optimal combination therapies.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".