Impact of Early Breast Cancer Screening on Mortality Among Young Survivors of Childhood Hodgkin’s Lymphoma
Bibliographic record
Abstract
BACKGROUND: Female survivors treated with thoracic radiation therapy (RT) for childhood cancer experience increased risks of breast cancer (BC). There are currently no data quantifying the potential mortality gains of early BC screening among such survivors. METHODS: A mathematical model of BC development was used to evaluate the marginal benefit of early-initiated screening of female survivors of adolescent Hodgkin's lymphoma (HL) starting at age 25 years on BC mortality compared with screening initiated at age 40 years. Sensitivity analyses were performed to evaluate the robustness of the estimates over a plausible range of conditions. RESULTS: For survivors treated at age 15 years, the absolute risk of BC mortality by age 75 years was predicted to decrease from 16.65% with no early screening to 16.28% (annual mammography), 15.40% (annual MRI), 15.38% (same-day annual mammography and MRI), and 15.37% (alternating mammography and MRI every six months). Approximately 80 patients would need to be invited to MRI-based screening to prevent one BC death. In sensitivity analyses, the number needed to invite to MRI-based screening to prevent one BC death ranged from 71 to 333. Combinations of MRI plus mammography were predicted to produce 99.52 false positives per 1000 screenings done between age 25 to 39 years. CONCLUSIONS: These findings are the first to indicate that early MRI-based screening should reduce BC mortality among women treated with RT for adolescent HL. The magnitude of this benefit is superior to that described for other accepted screening indications although MRI can produce a substantial rate of false-positive results.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".