Patterns of diagnostic imaging and cumulative radiation exposure among long-term young adult cancer survivors.
Bibliographic record
Abstract
6027 Background: Young adults surviving a diagnosis of malignancy have a considerable life expectancy, however, little is known about radiation exposure from diagnostic imaging in these patients. This study aims to describe patterns of imaging and radiation exposure in young adult cancer survivors (YAS) and cancer-free controls in Ontario, Canada. Methods: We conducted a population-based retrospective study. Young adults aged 20-44 diagnosed with an invasive malignancy between 1992 and 1999 who lived at least 5 years without recurrent disease were identified in the Ontario Cancer Registry. YAS were matched 1:5 to randomly selected cancer-free controls on calendar year of birth, sex, and geographic location. Radiological procedures were identified through Ontario Health Insurance Plan administrative data. The rate at which individuals underwent diagnostic procedures after surviving 5-years was compared between survivors and controls using Poisson regression. Cumulative radiation exposure due to computed tomography (CT) and standard x-ray were calculated. Results: 20,911 YAS and 104,524 controls had a median of 13.5 years observation time after cancer diagnosis/referent date. The rate of CT scanning after 5 year survival was higher in YAS (rate ratio= 3.59, 95% CI: 3.46 – 3.73) and varied according to malignancy type (Table). Over the course of diagnosis, treatment and surveillance to 10 years, the average YAS was exposed to 33.8 mSv of radiation, 4.4 times the radiation exposure an individual in the general population received. 47% of YAS cumulative dose was attributed to exposure 5 years or more after diagnosis. Conclusions: YAS undergo imaging and are exposed to diagnostic irradiation at a significantly higher rate than controls even after 5 years of recurrence free survival. Alternative imaging techniques not associated with exposure to radiation should be considered for these patients. [Table: see text]
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".