Cost‐effectiveness of early cancer surveillance for patients with Li–Fraumeni syndrome
Bibliographic record
Abstract
INTRODUCTION: Patients with germline TP53 pathogenic variants (Li-Fraumeni syndrome [LFS]) are at extremely high lifetime risk of developing cancer. Recent data suggest that tumor surveillance for patients with LFS may improve survival through early cancer detection. The objective of this study was to assess the cost-effectiveness of a cancer surveillance strategy for patients with LFS compared with those whose tumors present clinically. METHODS: A Markov decision analytic model was developed from a third-party payer perspective to estimate cost-effectiveness of routine cancer surveillance over a patient's lifetime. The model consisted of four possible health states: no cancer, cancer, post-cancer survivorship, and death. Model outcomes were costs (2015 United States Dollars [USD]), effectiveness (life years [LY] gained), and incremental cost-effectiveness ratio (ICER; change in cost/LY gained). One-way sensitivity analyses and probabilistic sensitivity analyses examined parameter uncertainty. RESULTS: The model showed a mean cost of $46 496 and $117 102 and yielded 23 and 27 LY for the nonsurveillance and surveillance strategies, respectively. The ICER for early cancer surveillance versus no surveillance was $17 125 per additional LY gained. At the commonly accepted willingness to pay threshold of $100 000/life-year gained, surveillance had a 98% probability of being the most cost-effective strategy for early cancer detection in this high-risk population. CONCLUSIONS: Presymptomatic cancer surveillance is cost-effective for patients with germline pathogenic variants in TP53. Lack of insurance coverage or reimbursement in this population may have significant consequences and leads to undetected cancers presenting in later stages of disease with worse clinical outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".