Costs of in-house genomic profiling and implications for economic evaluation: a case example of non-small cell lung cancer (NSCLC)
Bibliographic record
Abstract
OBJECTIVES: Genomic profiling in oncology is vital for determining eligible patients for mutation-specific targeted therapies. Use of commercial genomic testing has the potential to improve patient outcomes. Economic evaluations of in-house genomic profiling typically only include material costs while external commercial services include many other factors. Using non-small cell lung cancer (NSCLC) as an example, this study sought to characterize the unique challenges of costing testing services and their impact on results of economic evaluations. METHODS: Structured interviews with Canadian oncologists, pathologists, and laboratory directors were conducted to identify material and non-material costs associated with genomic-testing laboratories to allow estimation of a more complete cost of in-house testing, with NSCLC cost-per-test calculated using annual operational costs and NSCLC-specific testing volume. A health and budget impact model of in-house versus external commercial profiling services was used to compare the impact of non-material costs on results. RESULTS: In-house testing costs, limited to materials, was $133/single-gene test and $1,400/panel. For a laboratory running 1,300 in-house tests/year, total annual non-material costs included equipment maintenance ($6,842), labor ($502,313; technicians, administrative, and medical staff), shipping/reporting and software updates ($146,050), for an additional $519/test. The combined cost of $652/single-gene and $1,919/panel was compared to a cost of $6,194 for a commercial external test. Based on current Canadian testing patterns and anticipated utilization of external testing, inclusion of in-house non-material costs reduced the estimated 3-year budget impact by 12%. CONCLUSION: When conducting economic evaluation to assess the value of introducing external tests, it is critical that non-material costs of standard testing strategies be measured and incorporated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".