Pre or postnatal radiation exposure from diagnostic X-rays or CT scans and cancer risk : a systematic review and meta-analysis
Bibliographic record
Abstract
Background \nRadiological examination is a common diagnostic practice in modern medicine, they are not uncommonly performed during pregnancy or childhood. The potential biological effects of radiation to both the developing fetus and children are not always clear and remained controversial over many years. Physicians who care for these patients always find it difficult to evaluate the risk, and have misconceptions regarding the use of ionized radiation in pregnancy and children, which may delay the management process. \n \nObjective \nThis study has reviewed all recent published observational studies, and analyse any possible association of prenatal or postnatal X-ray exposure from diagnostic imaging and childhood cancer risk. \n \nMethods \nEligible epidemiological studies published between January 2000 and June 2013 were reviewed. These studies were found through electronic searches using Medline, PubMed, Embase, and Cochrane Database. Predetermined inclusion and exclusion criteria were applied to the identified articles \n \nResults \nTwenty-five articles with fourteen million participants were recruited. 17 out of 25 were case-control studies and 8 were cohort studies. All studies tried to prove an association between X-ray or CT scan exposure, and cancer of the haematopoietic system, brain and soft tissue regions. Results were summarized separately for their study methods, mode of radiation exposure and for each cancer outcome. The quality of the articles was accessed with the Newcastle-Ottawa scale. \nThe overall OR estimate from case-control studies showed postnatal X-ray exposure positively and significantly associated with leukaemia risk (OR 1.21; 95% CI: 1.10-1.32; I2 = 3%). Cancer risk other than leukaemia are lacking in case-control studies. Recent cohort studies also showed a small but significant increase risk of leukaemia and brain tumour from childhood CT scan exposure. \n \nConclusion \nThis analysis had shown a small but significant increase cancer risk from X-ray or CT scans exposure in postnatal period. Varies measures should be used to minimize the radiation dose in children during radiation exposure. As long as the radiological imaging is clinically indicated and performed using appropriate scanning protocol, the benefits of radiological imaging should far outweigh the small radiation risk.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.029 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".