Measuring the Population Impact of Introducing Stereotactic Ablative Radiotherapy for Stage I Non-Small Cell Lung Cancer in Canada
Bibliographic record
Abstract
BACKGROUND: The Cancer Risk Management Model (CRMM) was used to estimate the health and economic impact of introducing stereotactic ablative radiotherapy (SABR) for stage I non-small cell lung cancer (NSCLC) in Canada. METHODS: The CRMM uses Monte Carlo microsimulation representative of all Canadians. Lung cancer outputs were previously validated internally (Statistics Canada) and externally (Canadian Cancer Registry). We updated costs using the Ontario schedule of fees and benefits or the consumer price index to calculate 2013 Canadian dollars, discounted at a 3% rate. The reference model assumed that for stage I NSCLC, 75% of patients undergo surgery (lobectomy, sublobar resection, or pneumonectomy), 12.5% undergo radiotherapy (RT), and 12.5% undergo best supportive care (BSC). SABR was introduced in 2008 as an alternative to sublobar resection, RT, and BSC at rates reflective of the literature. Incremental cost effectiveness ratios (ICERs) were calculated; a willingness-to-pay threshold of $100,000 (all amounts are in Canadian dollars) per quality-adjusted life-year (QALY) was used from the health care payer perspective. RESULTS: The total cost for 25,085 new cases of lung cancer in 2013 was calculated to be $608,002,599. Mean upfront costs for the 4,318 stage I cases were $7,646.98 for RT, $8,815.55 for SABR, $12,161.17 for sublobar resection, $16,266.12 for lobectomy, $22,940.59 for pneumonectomy, and $14,582.87 for BSC. SABR dominated (higher QALY, lower cost) RT, sublobar resection, and BSC. RT had lower initial costs than SABR that were offset by subsequent costs associated with recurrence. Lobectomy was cost effective when compared with SABR, with an ICER of $55,909.06. CONCLUSION: The use of SABR for NSCLC in Canada is projected to result in significant cost savings and survival gains.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".