Predicting the Performance of a Genetic Testing Service for Cancer Susceptibility
Bibliographic record
Abstract
A genetic testing service can determine which members of a population might benefit most from cancer prevention. The eligibility criteria will affect the number of people who use a service and the proportion who test positive. This affects both the service's costs and benefits. The goal of this study was to create computer software that predicts the effect of eligibility restrictions on the performance of a genetic testing service. The software allows eligibility restrictions based on age, gender, and family history of disease. As performance measures, we considered the sensitivity and specificity of eligibility criteria to identify people with genetic cancer susceptibility, the likelihood of genetic susceptibility among people who are eligible for the service, and the likelihood of genetic susceptibility among people who are ineligible. We compared the performance predicted by our model with the observed performance of the Hereditary Cancer Program at the BC Cancer Agency, and studied the effects of changes to model parameters. There was good agreement between model predictions and observed outcomes, however, performance measures were affected by changes to the underlying model parameters. Computer software to predict the performance of a genetic testing service for cancer susceptibility is implemented on the website http://142.103.207.51:8080/gtsim.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".