Real-world outcomes associated with bevacizumab combined with chemotherapy in platinum-resistant ovarian Cancer
Bibliographic record
Abstract
OBJECTIVES: The addition of bevacizumab to chemotherapy for platinum-resistant (PL-R) ovarian cancer (OC) improved progression-free (PFS) but not overall survival (OS) in clinical trials. We explored real-world outcomes in Ontario, Canada, and compared survival in the pre- and post-bevacizumab era. METHODS: Administrative databases were utilized to identify all patients treated with bevacizumab for PL-R OC. Time on treatment (ToT) was used as surrogate for PFS. Median OS was determined using the Kaplan-Meier method. Factors associated with ToT/OS were identified using a Cox proportional hazard model. A before and after comparative effectiveness analysis was performed to determine mOS for patients treated pre- and post-bevacizumab approval. RESULTS: From 2017 to 2019, 176 patients received bevacizumab. Median ToT was 3 months and OS was 11 months. Sixty-four percent received liposomal doxorubicin and 34% received paclitaxel. ToT (6 vs 3 months; HR 0.44; p < 0.0001) and OS (14 vs 9 months; HR 0.45; p = 0.0089) were longer with bevacizumab/paclitaxel. OS was not significantly different pre- and post-bevacizumab funding (8 vs 9 months; HR 1.01; 0.937). Median OS increased for those receiving paclitaxel (6 vs 11 months), but those in the post group were younger, more likely to have undergone primary surgery and had less co-morbidities. CONCLUSION: Real-world outcomes with bevacizumab in PL-R OC are inferior to those in the pivotal clinical trial. Survival has not significantly improved since funding became publicly available, indicating a substantial efficacy-effectiveness gap between trial and real-world outcomes. Median OS and ToT were significantly better when bevacizumab was given with paclitaxel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".