Patterns of first-line systemic therapy delivery and adoption of bevacizumab in advanced ovarian cancer in Ontario, Canada.
Bibliographic record
Abstract
292 Background: Initial treatment of epithelial ovarian cancer (EOC) consists of combination of cytoreductive surgery (CSR) and/or chemotherapy. Targeted therapies such as bevacizumab have shown to improve outcomes in a subset population with high-risk features. Real-world patterns of systemic therapy delivery in EOC in the modern era are not well understood. Our objective is to evaluate the patterns of first-line systemic treatment of advanced EOC in Ontario, focusing on adoption of bevacizumab, which was approved for use in 2016. Methods: We conducted a retrospective, population cohort study using administrative databases held at the ICES in Ontario, Canada. Patients diagnosed with non-mucinous EOC between 2014 and 2018 were identified from the Ontario Cancer Registry; early-stage disease was excluded. Information on systemic therapy was obtained from Activity Level Reporting and New Drug Funding Program databases. Provider of care (gynecologic oncologist vs medical oncologist) information was obtained from billing codes. Academic cancer centers were identified using validated systemic facility codes from Cancer Care Ontario. Statistical analyses include descriptive statistics, t-tests, and multivariable logistic regression using SAS. Results: Out of 4,680 cases diagnosed with EOC during the study period, 3,632 (77.6%) were considered advanced stage. Median age of cohort was between 65-70, and the majority had Charlson score of 1-2 (97%) and are urban (91.8%). A total of 3,181 (87.6%) patients underwent CRS and 2,722(74.9%) patients underwent chemotherapy. Of those who received chemotherapy, 1,259 (46.2%) received neoadjuvant chemotherapy, 1,012 (37.2%) received upfront CRS, and 451(16.5%) received chemotherapy only. The majority of chemotherapy was delivered by gynecologic oncologists (60.6%) and in academic cancer centres (61.7%). There was no significant difference in use of neoadjuvant chemotherapy between medical oncologists and gynecologic oncologists (p = 0.67). Only 53 chemotherapy patients (1.9%) received bevacizumab containing-regimen in the first-line setting. Medical oncologists were 4 times more likely to administer bevacizumab-containing regimen compared to gynecologic oncologists (OR 4.03, 95% CI.29 – 7.36) after adjusting for age, stage, Charlson score and rurality score on logistic regression. Delivery of bevacizumab is relatively higher in non-academic cancer centres (OR 2.61, 95% CI 2.32- 2.94) while 83% of intraperitoneal chemotherapy is delivered in academic cancer centres. Conclusions: Patterns of care of EOC in Ontario remain heterogenous between care providers and institutions, while uptake of bevacizumab for first-line treatment of EOC remains low. Factors leading to low uptake and real-world outcomes should be explored.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".