Vitamin D deficiency is an indicator of high mortality
Bibliographic record
Abstract
Studies in recent years indicate that vitamin D deficiency has a great impact on the overall health and life expectancy of a person, and vitamin D preparations can be successfully used to prevent and treat a wide range of diseases in adults and children. At the same time, in the Russian Federation, the prevalence of vitamin D deficiency and deficiency reaches 84 %. In this regard, statistical studies of the mortality of the population of Europe (including Russia), the USA and Canada, depending on the prevalence of vitamin D deficiency, have been conducted. The results showed that the prevalence of vitamin D deficiency in the population of these countries is associated with statistically significant directly proportional relationships (p = 0.002‑0.03) with total mortality from non-communicable diseases, mortality from coronary heart disease, stroke, chronic obstructive pulmonary disease, diabetes and COVID-19. In this regard, it seems absolutely necessary to radically change the attitude of the population, authorities, medical workers and the public to the problem of D-vitamin deficiency of the population of the Russian Federation. It is necessary to prepare and implement federal and regional programs for the D-vitaminization of the population of the Russian Federation, including a large-scale information campaign on the benefits of vitamin D, monitoring the level of vitamin D in the most vulnerable categories of the population and their treatment with vitamin D supplements, which will improve the health status and reduce premature mortality of the population of the Russian Federation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".