Cost-Effectiveness Analysis of Using Loss of Heterozygosity to Manage Premalignant Oral Dysplasia in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Management of low-grade oral dysplasias (LGDs) is complicated, as only a small percentage of lesions will progress to invasive disease. The current standard of care requires patients to undergo regular monitoring of their lesions, with intervention occurring as a response to meaningful clinical changes. Recent improvements in molecular technologies and understanding of the biology of LGDs may allow clinicians to manage lesions based on their genome-guided risk. METHODS: We used a decision-analytic Markov model to estimate the cost-effectiveness of risk-stratified care using a genomic assay. In the experimental arm, patients with LGDs were managed according to their risk profile using the assay, with low- and intermediate-risk patients given longer screening intervals and high-risk patients immediately treated with surgery. Patients in the comparator arm had standard care (biannual follow-up appointments at an oral cancer clinic). Incremental costs and outcomes in life-years gained (LYG) and quality-adjusted life-years (QALY) were calculated based on the results in each arm. RESULTS: The mean cost of assay-guided management was $8,123 (95% confidence interval [CI] $2,973 to $23,062 in 2013 Canadian dollars) less than the cost of standard care. This difference was driven largely by reductions in resource use among people who did not develop cancer. Mean incremental effectiveness was 0.18 LYG (95% CI 0.08 to 0.39) or 0.64 QALY (95% CI 0.46 to 0.89). Sensitivity analysis suggests that these findings are robust to both expected and extreme variation in all parameter values. CONCLUSION: Use of the assay-guided management strategy costs less and is more effective than standard management of LGDs. IMPLICATIONS FOR PRACTICE: The findings of this study strongly suggest that the use of a risk-stratification method such as a genomic assay can result in improved quality-adjusted survival outcomes for patients with low-grade oral dysplasia (LGD). The use of such an assay in this study provides "precision medicine," allowing for a change in follow-up frequency or early intervention as compared with current standard care. As genomic technologies become more common in cancer care, it is hoped that such an assay, once validated, will become part of a new model for the standard management of LGDs in similar health systems.
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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) | 0.001 | 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".