Health and Budget Impact of Liquid-Biopsy-Based Comprehensive Genomic Profile (CGP) Testing in Tissue-Limited Advanced Non-Small Cell Lung Cancer (aNSCLC) Patients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Molecular genetic testing using tissue biopsies can be challenging for patients due to unfavorable tumor sites, the invasive nature of a tissue biopsy, and the added time of booking a repeat biopsy (re-biopsy). Centers in Canada have found insufficient tissue rates to be approximately 10%, and even among successful biopsies, insufficient DNA in tissue samples is approximately 16%, triggering the lengthy process of re-biopsies. Using aNSCLC as an example, this study sought to characterize the health and budget impact of alternative liquid-biopsy(LBx)-based comprehensive genomic profile (CGP) testing in tissue-limited patients (TL-LBx-CGP) from a Canadian publicly funded healthcare perspective. MATERIAL AND METHODS: An economic model was developed to estimate the incremental cost and life-years gained as a population associated with adopting TL-LBx-CGP. The eligible patient population was modeled using a top-down epidemiological approach based on the published literature and expert clinician input. Treatment allocation was modeled based on biomarker prevalence in the published literature, and the availability of funded therapies. Costs included molecular testing, as well as drug, administrative, and supportive costs, and relevant health data included median overall survival and median progression-free survival data. RESULTS: Incorporation of TL-LBx-CGP demonstrated an overall impact of $14.7 million with 168 life-years gained to the Canadian publicly funded healthcare system in the 3-year time horizon.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".