Clinical outcomes and cost-effectiveness of breast cancer screening for childhood cancer survivors treated with chest radiation: A comparative modeling study.
Bibliographic record
Abstract
6525 Background: Survivors of childhood cancer previously treated with chest radiation face elevated breast cancer risk similar to BRCA1 carriers. Children’s Oncology Group (COG) guidelines recommend annual mammography with breast MRI, yet the benefits and costs of various screening strategies are uncertain. Methods: We used two breast cancer simulation models (Model 1 and 2) from the Cancer Intervention and Surveillance Modeling Network (CISNET) and data from the Childhood Cancer Survivor Study to reflect high breast cancer and competing mortality risks among survivors. We simulated 3 screening strategies: annual mammography with MRI starting at age 25 (COG25), annual MRI starting at 25 (MRI25), and biennial mammography starting at 50 (Mammo50). Performance of mammography+/-MRI was based on published studies in BRCA1/2 carriers who have similar cancer risk. Costs and quality of life weights were based on US averages and published studies. Results: Among a simulated cohort of 25-year-old survivors treated with chest radiation, the lifetime breast cancer mortality risk in the absence of screening was 10-11% across models. Compared to no screening, Mammo50, MRI25, and COG25 screening avert approximately 23-25%, 56-62% and 56-71% of deaths, respectively; averted deaths for COG25 compared to MRI25 were higher in Model 1 than Model 2 (9% vs. <1%). In Model 1, both MRI25 and COG25 were cost-effective; in Model 2, MRI25 was preferable (more effective, less costly than COG25). Conclusions: Compared to no screening, initiating annual screening at younger ages for at-risk survivors averts >50% of breast cancer deaths and is cost-effective. Additional data on test performance are needed to inform recommendations on screening modality. [Table: see text]
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".