Estimation of Cancer Mortality Associated with Repetitive Computed Tomography Scanning
Bibliographic record
Abstract
RATIONALE: Low-dose radiation from computed tomography (CT) may increase the risk of certain cancers, especially in children. OBJECTIVE: We sought to estimate the excess all-cause and cancer-specific mortality, which may be associated with repeated CT scanning of patients with cystic fibrosis (CF). METHODS: The radiation dose was calculated for a published CF surveillance CT scanning protocol of biennial CT scans, and the risk per scan was estimated using atom-bomb survivor data. A computational model was developed to calculate the excess mortality in a CF cohort associated with radiation from the CT scan and to evaluate the effects of background survival, scanning interval, and level of CT radiation used. The model assumed that there would be no survival benefits associated with repeated surveillance CT scanning. RESULTS: The average radiation dose for the published CT protocol was 1 mSv. Survival reduction associated with annual scans from age 2 yr until death was approximately 1 mo and 2 yr for CF cohorts, with a median survival of 26 and 50 yr, respectively. Corresponding cumulative cancer mortality was approximately 2 and 13% at age 40 and 65 yr, respectively. Biennial CT scanning reduced all-cause and cumulative cancer mortality by half. CONCLUSION: Routine lifelong annual CT scans carry a low risk of radiation-induced mortality in CF. However, as the overall survival increases for patients with CF, the risk of radiation-induced mortality may modestly increase. These data indicate that radiation dose must be considered in routine CT imaging strategies for patients with CF, to ensure that benefits outweigh the risks.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".