Comprehensive Genomic Profiling for Non-Small-Cell Lung Cancer: Health and Budget Impact
Bibliographic record
Abstract
Background: Single-gene tests and hotspot panels targeting specific subsets of biomarkers constitute the Canadian genomic testing landscape for non-small-cell lung cancer (nsclc). However, newer testing options such as comprehensive genomic profiling (cgp) offer improved detection rates and identification of multiple classes of genomic alterations in a single assay, minimizing tissue requirements and turnaround time. The objective of the present analysis was to assess the health and budget impacts of adopting cgp testing for nsclc in Canada. Methods: This study assessed the impact of funding the cgp tests FoundationOne CDx and FoundationOne Liquid (Foundation Medicine, Cambridge, MA, U.S.A.) over a 3-year time horizon using a Canadian societal perspective for Ontario. Conventional testing strategies were summarized into two reference scenarios: a series of single-gene tests only, and reflex single-gene testing followed by a hotspot panel for negative results. Four adoption scenarios for cgp testing were considered: replacing all single-gene and hotspot panel testing, replacing hotspot panel testing only, use after negative single-gene and hotspot testing, and use of FoundationOne Liquid in individuals with insufficient tissue for conventional testing. Results: When cgp testing was assumed to replace all conventional testing with 50% uptake, the budget impact per person per year ranged from $0.71 to $0.87, depending on the reference scenario, with a 3-year gain of 680.9 life-years and 3831 working days over the full cohort. Conclusions: Given the present testing landscape for patients with nsclc in Canada, listing cgp testing could optimize the selection of appropriately targeted treatments, and thus add life-years and productivity for this population, with a minimal budget impact.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".