Patterns of First-Line Systemic Therapy Delivery and Outcomes in Advanced Epithelial Ovarian Cancer in Ontario
Bibliographic record
Abstract
BACKGROUND: First-line treatment of epithelial ovarian cancer (EOC) consists of a combination of cytoreductive surgery and platinum-based chemotherapy. Recently, targeted therapies such as bevacizumab have been shown to improve oncologic outcomes in a subset of a high-risk population. The objective of this study is to evaluate the patterns of practice and outcomes of first-line systemic treatment of advanced EOC, focusing on the adoption of bevacizumab. METHODS: A population cohort study was conducted using administrative data in Ontario, Canada. Patients diagnosed with advanced stage non-mucinous EOC between 2014 and 2018 were identified. Datasets were linked to obtaining information on first-line treatment including surgery, systemic therapy, providers of care, systemic therapy facilities, and acute care utilization (emergency department (ED) visits and hospitalizations) during systemic treatment. Multivariate logistic regression was used to determine factors associated with systemic therapy utilization. RESULTS: Among 3726 patients with advanced EOC, 2838 (76%) received chemotherapy: 1316 (47%) received neoadjuvant chemotherapy, 1060 (37%) underwent primary cytoreductive surgery followed by chemotherapy, and 462 (16%) received chemotherapy only. The median age was 67 (range: 20-100). Most chemotherapies were prescribed by gynecologic oncologists (60%) and in level 1 academic cancer centres (58%). Only 54 patients (3.1%) received bevacizumab in the first-line setting after its approval in Ontario in 2016. Bevacizumab was more likely to be administered by medical oncologists compared to gynecologic oncologists (OR 3.95, 95% CI 2.11-7.14). In total, 1561 (55%) and 1594 (56%) patients had at least one ED visit and/or hospitalization during systemic treatment, respectively. The most common reasons for ED visits were fever and bowel obstruction. CONCLUSION: Patterns of care for EOC in Ontario differed between care providers. The uptake of bevacizumab for first-line treatment of EOC was low. Acute care utilization related to EOC was high.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".