Comparative efficacy and safety of low-dose versus high-dose bevacizumab in ovarian cancer: An indirect treatment comparison
Bibliographic record
Abstract
OBJECTIVE: First-line therapy for ovarian cancer involves cytoreductive surgery and platinum-based chemotherapy, with or without bevacizumab. Bevacizumab can be administered at low (7.5 mg/kg every three weeks [Q3W]) or high dose (15 mg/kg Q3W). This study compared the efficacy and safety of these dosing strategies. METHODS: Systematic literature review of Embase, MEDLINE®, and CENTRAL (18/09/2023) identified randomized controlled trials (RCTs) evaluating bevacizumab versus any therapy or control in ovarian, fallopian tube, or primary peritoneal cancer. Indirect treatment comparisons (ITC) of response, survival, and safety outcomes were performed, including sensitivity/subgroup analyses adjusting for heterogeneity. RESULTS: Six RCTs (sample size: 24-1528 patients) were included for ITC. Five evaluated high-dose bevacizumab with chemotherapy. The common comparator was carboplatin + paclitaxel. Trials mainly included stage III (n = 4) or stage II-III (n = 1) ovarian cancer patients; one did not report cancer stage. Primary analyses showed no significant differences between low- versus high-dose bevacizumab for partial response (risk ratio [95 % confidence interval]: 0.66 [0.42, 1.02]), complete response (1.76 [0.76, 4.11]), objective response rate (1.01 [0.63, 1.61]), progressive disease (1.08 [0.38, 3.10]), clinical benefit (0.89 [0.76, 1.03]), any grade ≥ 3 adverse event (1.53 [0.96, 2.44]), specific grade ≥ 3 adverse events, overall survival (hazard ratio: 0.93 [0.77, 1.13]), or progression-free survival (1.02 [0.86, 1.22]). Sensitivity and subgroup analyses confirmed findings. CONCLUSIONS: This ITC found no significant difference in clinical outcomes between low- and high-dose bevacizumab combination therapy. Despite limitations of small sample size and heterogeneities, findings suggest that bevacizumab dose may not significantly impact ovarian cancer outcomes.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".