Real-World Treatment Patterns, Survival, and Costs for Ovarian Cancer in Canada: A Retrospective Cohort Study Using Provincial Administrative Data
Bibliographic record
Abstract
Background: In 2020, approximately 3100 Canadian women were diagnosed with ovarian cancer (OC), with 1950 women dying of this disease. Prognosis for OC remains poor, with 70% to 75% of cases diagnosed at an advanced stage and an overall 5-year survival of 46%. Current standard of care in Canada involves a combination of cytoreductive surgery and platinum-based chemotherapy. Objective: There are few studies reporting current OC costs. This study sought to determine patient characteristics and costs to the health system for OC in Ontario, Canada. Methods: Women diagnosed with OC in Ontario between 2010 and 2017 were identified. The cohort was linked to provincial administrative databases to capture treatment patterns, survival, and costs. Overall total and mean cost per patient (unadjusted) were reported in 2017 Canadian dollars, using a macro-based costing methodology called GETCOST. It is programmed to determine the costs of short-term and long-term episodes of health-care resources utilized. Results: Of the 2539 OC patients included in the study, the mean age at diagnosis was 60.4±11.35 years. The majority were diagnosed with stage III disease (n=1247). The only treatment required for 74% of stage I patients and 54% of stage II patients was first-line (1L) platinum chemotherapy; in advanced stages (III/IV) 24% and 20%, respectively, did not receive further treatment after 1L therapy. The median overall survival (mOS) for the whole cohort was 5.13 years. Survival was highest in earlier stage disease (mOS not reached in stage I/II), and dropped significantly in advanced stage patients (stage III: mOS=4.09 years; stage IV: mOS=3.47 years). Overall mean costs in patients stage I were CAD $58 099 compared to CAD $124 202 in stage IV. Discussion: The majority of OC patients continue to be diagnosed with advanced disease, which is associated with poor survival and increased treatment costs. Increased awareness and screening could facilitate diagnosis of earlier stage disease and reduce high downstream costs for advanced disease. Conclusion: Advanced OC is associated with poor survival and increased costs, mainly driven by hospitalizations or cancer clinic visits. The introduction of new targeted therapies such as olaparib could impact health system costs, by offsetting higher downstream costs while also improving survival.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".