Medical exposure to ionizing radiation and brain tumour risk – analyses of data from five Interphone countries
Bibliographic record
Abstract
Background and Aims: Medical ionizing radiation represents an indispensable tool in modern medicine and it is the largest human-made source of radiation exposure. At moderate to high doses, ionising radiation is however a known risk factor for cancer and it is the only well established risk factor for brain tumours (UNSCEAR, 2008). Despite extensive knowledge of radiation risks gained through epidemiologic investigations and mechanistic considerations the health effects of low-level radiation exposure are still poorly understood (1). We therefore evaluated the risk of brain tumours in relation to reported medical radiation exposure. Methods: This analysis is based on pooled datasets from five Interphone countries (Australia, Canada, France, Israel and New Zealand) information on all medical procedures involving ionizing radiation exposure during participant’s lifetime was obtained by questionnaire, including year, anatomical region exposed and reason for examination or treatment for radiotherapy. Estimated dose to the brain was calculated for each procedure based on time and country specific average dose levels available in publications of the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR)(2). Analyses are based on unconditional logistic regression, stratified on age, sex, country/region. All analyses are adjusted for socio-economic status. Results: The analyses included 905 cases of glioma, 916 cases of meningioma, 423 cases of acoustic neuroma and 6840 controls. Odds ratios (ORs) and 95% CI (confidence intervals) will be presented by cumulative estimated level of medical radiation for each of these tumour types Conclusions: Results from these analyses will contribute to the body of evidence on potential health effects of low level medical exposure to ionising radiation.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".