Statistical power of epidemiological studies of low dose levels of ionizing radiation and cancer
Bibliographic record
Abstract
The purpose of this research is to inspect the statistical power of studies that investigate\nthe effects of low dose ionizing radiation on the incidence of/mortality from cancer. I use\na procedure proposed in a similar study to handle a problem regarding the incidence of\nchildhood leukemia from background ionizing radiation. I study this procedure critically\nand make some adjustments to make its performance better. I also propose some substitute\nmethods to the methods proposed in the aforementioned reference in order to calculate\nthe power. In addition, I propose other methods not used in the study mentioned above.\nI evaluate the efficiency of my proposed approaches using simulated data. The improved\nmethod can be applied to the National Dose Registry of Canada (NDR) to produce the\npower curves. The outcomes then can be used to propose the most suitable study design.\nSome of the previous epidemiological studies based on NDR can also be evaluated in terms\nof power.
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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 | 0.225 | 0.514 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".