Radon exposure and COVID-19 mortality in pre-vaccination period: What links might exist?
Bibliographic record
Abstract
Radon, a naturally occurring radioactive gas known for its health risks, has recently gained attention for its potential protective role against COVID-19 mortality. This cross-sectional ecological study examined the relationship between indoor radon exposure and COVID-19 mortality rates across eight countries, including several European nations, the United States, and the State of Kerala, India, during the pre-vaccination period. The analyzed data on the subject were derived from recent scientific publications. The environmental aspect was represented by the variable "indoor radon concentration or probability of exceeding a radon concentration in indoor air," depending on data availability. Using national radon surveys and COVID-19 mortality statistics, statistical analyses, including Spearman's correlation and Kendall Tau, were conducted between March and December 2020. The findings revealed a consistent negative correlation between radon concentrations and COVID-19 mortality rates, indicating that higher radon concentrations were associated with lower mortality rates. Regions such as Finland and Sweden, where radon exposure was relatively high, experienced significantly lower mortality. With Sweden and Finland showing a mortality risk reduction factor of respectively 1,42 and 5,47 during the first wave compared to the UK where Radon levels are very low. Although the findings are not overwhelmingly strong, the data suggest that radon exposure may have a mitigating effect on COVID-19 mortality.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".