Cost of illness of renal cell carcinoma in Canada
Bibliographic record
Abstract
15560 Background: Renal cell carcinoma (RCC) is the most common form of kidney cancer. RCC patients have limited treatment options and low survival rates, particularly for advanced-staged patients. Despite its importance, data on the economic burden of RCC are limited. Methods: A prevalence-based approach was used to estimate the aggregate annual societal cost burden of RCC in Canada. Key relationships represented in the model include the annual number of patients treated for RCC by age group and cancer stage; utilization of cancer treatments; unit costs; work-days missed, and wage rates. Results: The annual prevalence of RCC in Canada in 2005 was estimated to be 17,845 cases. The associated annual burden of RCC (Canadian $2005) was approximately $357 million ($19,981 per patient). Health-care costs and lost productivity accounted for 65.6% ($234 million) and 34.4% ($123 million) of the total, respectively. Reflecting its higher prevalence, the total cost associated with Stage II RCC accounted for the greatest share (67%) followed by Stage I, Stage III, and Stage IV RCC, at 19.8%, 11.6% and 1.6%, respectively. Conclusions: The economic burden of RCC in Canada is substantial at a cost of $357M which represents 2% of the total cost of illness of cancer in Canada ($16.64B, inflated to $2005). Interventions to reduce the prevalence of RCC have the potential to yield considerable economic benefits. [Table: see text]
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".