Potential budget impact of capecitabine versus 5-FU/LV as adjuvant chemotherapy for stage III (SIII) colon cancer in Canada and its provinces
Bibliographic record
Abstract
6581 Background: Adjuvant chemotherapy for SIII colon cancer is an accepted standard of care. Oral Capecitabine (CAP) has been shown to be at least equivalent and possibly superior to 5FU/LV (Mayo regimen) with regards to a superior relapse free survival. This new option is associated with a higher drug cost but improved toxicity profile and appears to be cost-effective (CE). An economic analysis was undertaken to examine the potential budget impact for CAP in Canada and its provinces for 2007 onwards. Methods: A previously developed cost-effectiveness model was adapted to a prevalence perspective to project the net budgetary impact of CAP over a 5 year horizon. The projected population and incidence of colon cancer for each Canadian province from 2007–2016 was obtained and the proportion of patients with SIII colon cancer suitable for adjuvant chemotherapy was estimated from the literature. The average budget impact in the first 5 years (start up phase) and subsequent years (steady state) was assessed in Canadian $. Results: The projected average annual impact for Canada is 13.9 million (M) during the start up phase and $11.8 M during the steady state phase (NL $210K, PEI $63K, NS $424K, NB $282K, QC $2.92M, ON $4.66M, MB $464K, SK $336K, AB $993K and BC 1.45M). Budget impact is greater during the initial start-up phase (2007–11), as the steady state impact (2012–16) includes relapses avoided over a 5-year period. Sensitivity analyses for key parameters will be provided. Conclusions: The annual budget impact of CAP decreases over time and reaches a steady state after 5 years when the full impact of decreased recurrences is captured. As CAP appears to be CE, budget impact analysis has the potential to assist in the planning of healthcare funding resources regarding this treatment option. [Table: see text]
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Simulation or modeling | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Simulation or modeling | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Simulation or modeling | high |
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".