Cost Savings of Expedited Care with Upfront Next-Generation Sequencing Testing versus Single-Gene Testing among Patients with Metastatic Non-Small Cell Lung Cancer Based on Current Canadian Practices
Bibliographic record
Abstract
This study assessed the total costs of testing, including the estimated costs of delaying care, associated with next-generation sequencing (NGS) versus single-gene testing strategies among patients with newly diagnosed metastatic non-small cell lung cancer (mNSCLC) from a Canadian public payer perspective. A decision tree model considered testing for genomic alterations using tissue biopsy NGS or single-gene strategies following Canadian guideline recommendations. Inputs included prevalence of mNSCLC, the proportion that tested positive for each genomic alteration, rebiopsy rates, time to test results, testing/medical costs, and costs of delaying care based on literature, public data, and expert opinion. Among 1,000,000 hypothetical publicly insured adult Canadians (382 with mNSCLC), the proportion of patients that tested positive for a genomic alteration with an approved targeted therapy was 38.0% for NGS and 26.1% for single-gene strategies. The estimated mean time to appropriate targeted therapy initiation was 5.1 weeks for NGS and 9.2 weeks for single-gene strategies. Based on literature, each week of delayed care cost CAD 406, translating to total mean per-patient costs of CAD 3480 for NGS and CAD 5632 for single-gene strategies. NGS testing with mNSCLC in current Canadian practice resulted in more patients with an identified mutation, shorter time to appropriate targeted therapy initiation, and lower total testing costs compared to single-gene strategies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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, 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".