Real-world outcomes associated with bevacizumab combined with chemotherapy in platinum-resistant ovarian cancer.
Bibliographic record
Abstract
5555 Background: In the pivotal Aurelia study, addition of bevacizumab (bev) to physician’s choice chemotherapy (paclitaxel, liposomal doxorubicin or topotecan) for platinum-resistant (PL-R) ovarian cancer (OC) was associated with improved progression-free survival (PFS; 6.7 vs 3.4 mos; hazard ratio (HR) 0.48, p = 0.001) but not overall survival (OS; 16.6 vs 13.3 mos HR 0.85, p = 0.174); the latter finding may relate to extensive crossover. In an exploratory subgroup analysis by treatment arm, benefits were particularly marked for bev + paclitaxel where median PFS (mPFS) increased from 3.9 to 10.4 mos (HR 0.46; 95%CI 0.30-0.71) and increases in OS approached statistical significance (HR 0.65; 95%CI, 0.42-1.02; 22.4 v 13.2 mos). Here we describe utilization of bev for PL-R OC and outcomes in routine clinical practice. Methods: The Ontario Cancer Registry and the New Drug Funding Program databases were utilized to identify all patients treated with bev plus chemotherapy (paclitaxel, liposomal doxorubicin or topotecan) for PL-R OC following its approval in 2017. Time on treatment (ToT) was defined as time from first to last bev treatment; this was used as a surrogate for PFS in routine practice. Median OS (mOS) was determined using the Kaplan-Meier method. Factors associated with ToT and OS were identified using a Cox proportional hazard model. A before and after comparison analysis was performed to determine mOS for patients treated pre- (2011-2017) and post-bev (2017-2019) approval. Results: From Oct 2017 to Dec 2019, 180 patients received bev + chemotherapy for first-line PL-R OC. Mean age was 63 years old, and 80% had serous OC. Bev was most often combined with liposomal doxorubicin (64 %) followed by paclitaxel (34%) and topotecan (2%). Median ToT was 3 months and OS was 11 months. ToT and OS were longer in patients who received paclitaxel as chemotherapy backbone (5 mos [ToT]; 14 mos [OS]) than those who received bev with liposomal doxorubicin (2 mos; 9 mos) or topotecan (2 mos; 6 mos). In multivariable models, ToT was superior in patients who received bev + paclitaxel than bev + liposomal doxorubicin (HR 0.40; 95%CI 0.28-0.57; p < 0.0001), and worse with longer time from diagnosis to bev start (1.03; 1.01-1.05; p = 0.0120). OS was also significantly longer in those who received paclitaxel vs liposomal doxorubicin (HR 0.54; 95%CI 0.30-0.98; p = 0.043). In a before and after analysis, patients treated in the pre- (n = 1290) and post-bev (n = 360) era had mOS of 8 and 9 months respectively. Post mOS increased for patients receiving paclitaxel (7 vs 12 months) but not with liposomal doxorubicin (9 vs 7 months). Conclusions: ToT and OS associated with bev for PL-R OC are shorter in a real-world population compared to results reported in AURELIA. ToT and OS were longer with bev + paclitaxel than with other chemotherapy agents.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".