Dose-response relationships for radiation-related heart disease: Impact of uncertainties in cardiac dose reconstruction
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Radiation-related heart disease (RRHD) can occur many decades after thoracic radiotherapy for Hodgkin lymphoma (HL) or childhood cancer (CC). To quantify the likely risk of RRHD for patients treated today, dose-response relationships derived from patients treated in previous decades are used. Publications presenting these dose-response relationships usually include estimates of uncertainties in the risks but ignore the effect of uncertainties in the reconstructed cardiac doses. MATERIALS/METHODS: We assessed the systematic and random uncertainties in the reconstructed doses for published dose-response relationships for RRHD risk in survivors of HL or CC. Using the same reconstruction methods as were used in the original publications, we reconstructed mean heart doses and, wherever possible, mean left-ventricular doses for an independent case-series of test patients. These patients had known, CT-based, cardiac doses which were compared with the reconstructed doses to estimate the magnitude of the uncertainties and their effect on the dose-response relationships. RESULTS: For all five reconstruction methods the relationship between reconstructed and CT-based doses was linear. For all but the simplest reconstruction method, the dose uncertainties were moderate, the effect of the systematic uncertainty on the dose-response relationships was less than 10%, and the effects of random uncertainty were small except at the highest doses. CONCLUSIONS: These results increase confidence in the published dose-response relationships for the risk of RRHD in HL and CC survivors. This may encourage doctors to use these dose-response relationships when estimating individualised risks for patients-an important aspect of personalising radiotherapy treatments today.
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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 | 0.042 | 0.205 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".