Cost-minimization analysis of genetic testing versus clinical screening of at-risk relatives for familial adenomatous polyposis.
Bibliographic record
Abstract
OBJECTIVE: Familial adenomatous polyposis (FAP) is a well-known hereditary colorectal cancer-predisposing syndrome. Genetic testing for colorectal cancer risk is now part of standard medical practice, but very little is known about the economic impact of this technology. The aim of this study was to assess, from a healthcare system perspective, the direct costs of two strategies for screening at-risk relatives of FAP patients: clinical screening versus genetic testing for FAP. METHODS: A systematic review of the literature was carried out. Additional information was gathered from experts in research and clinical laboratories and in hospital departments. A decision tree was constructed to compare per-person and per-family costs of the two strategies for screening at-risk relatives of FAP patients. Sensitivity analysis was performed to assess the stability of the model across the full range of plausible values for all key parameters. RESULTS: According to the decision analysis, with FAP screening starting at puberty, the average screening costs are $3,181 and $2,259 (Canadian dollars), respectively, for the clinical screening and the genetic testing strategies. Genetic screening is cost saving up to a first screening age of 36. Sensitivity analysis shows that the results of the baseline analysis hold across a variety of assumptions concerning the parameter values. CONCLUSIONS: The genetic testing strategy is cost saving relative to the clinical screening alternative. Apart from its lower costs, it is associated with many other benefits. Accordingly, under predefined conditions, predictive genetic testing seems to be the optimal screening strategy for FAP.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".