Exposure to low-dose radiation in occupational settings and ischaemic heart disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Ionising radiation is a human carcinogen, but the evidence is less clear that exposure to low-dose ionising radiation (LDIR) increases the risk of adverse cardiovascular outcomes. We synthesised the literature of chronic occupational exposure to LDIR and cardiovascular disease, particularly for ischaemic heart disease (IHD).The literature search was conducted using three databases including studies published between 1990 and 2022. A quality assessment of the studies was completed using the Office of Health and Assessment and Translation Risk of Bias Rating Tool. We conducted meta-analyses for IHD mortality using random effects models using measures of excess relative risk per sievert (ERR/Sv) obtained from internal cohort comparisons, as well as with standardised mortality ratios (SMRs) from external cohort comparisons.We identified 2189 articles, and of these, 26 provided data on IHD and were retained. Most studies were classified as having a 'moderate' level of risk of bias. Fourteen and 10 studies reporting external radiation doses were included in meta-analyses using SMR and ERR/Sv, respectively. The meta-summary SMR was 0.81 (95% CI 0.74 to 0.89) with evidence of reduced risk but high heterogeneity across studies. For internal cohort measures, the summary ERR/Sv for a lagged exposure of 10 years was 0.10 (95% CI 0.01 to 0.20) with low heterogeneity. The subgroup analysis by lagged exposure time showed the strongest association were for the 15 and 20 years lag.Our findings suggest that occupational exposure to LDIR increases the risk IHD mortality and highlight the relevance of internal cohort comparisons.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.040 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".