Association of ionizing radiation dose from common medical diagnostic procedures and lymphoma risk in the Epilymph case-control study
Bibliographic record
Abstract
Medical diagnostic X-rays are an important source of ionizing radiation (IR) exposure in the general population; however, it is unclear if the resulting low patient doses increase lymphoma risk. We examined the association between lifetime medical diagnostic X-ray dose and lymphoma risk, taking into account potential confounding factors, including medical history. The international Epilymph study (conducted in the Czech-Republic, France, Germany, Ireland, Italy, and Spain) collected self-reported information on common diagnostic X-ray procedures from 2,362 lymphoma cases and 2,465 frequency-matched (age, sex, country) controls. Individual lifetime cumulative bone marrow (BM) dose was estimated using time period-based dose estimates for different procedures and body parts. The association between categories of BM dose and lymphoma risk was examined using unconditional logistic regression models adjusting for matching factors, socioeconomic variables, and the presence of underlying medical conditions (atopic, autoimmune, infectious diseases, osteoarthritis, having had a sick childhood, and family history of lymphoma) as potential confounders of the association. Cumulative BM dose was low (median 2.25 mGy) and was not positively associated with lymphoma risk. Odds ratios (ORs) were consistently less than 1.0 in all dose categories compared to the reference category (less than 1 mGy). Results were similar after adjustment for potential confounding factors, when using different exposure scenarios, and in analyses by lymphoma subtype and by type of control (hospital-, population-based). Overall no increased risk of lymphoma was observed. The reduced ORs may be related to unmeasured confounding or other sources of systematic bias.We found little evidence that chronic medical conditions confound lymphoma risk and medical radiation associations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".