Bibliographic record
Abstract
Regarding Bermejo-Martin and colleagues' article [1] and my comments [2], the authors concluded in their response that 'vitamin D should thus be considered in the context of a wider spectrum of factors influencing severe disease'. Although this is a reasonable proposal, it should be emphasized that vitamin D has been recognized as an important immuno-modulating factor [3], and studies show that both obesity [4] and seasonal sunlight deprivation [5] play important roles in the severity of influenza, not just in the western countries, where obesity is widely present, but worldwide. Overall better living conditions coupled with a disproportionately better health-care system could explain the absence of a significantly higher incidence of obesity-related critically ill H1N1 patients in the western versus the developing countries. Regarding the exposure to sunlight, which is well correlated with vitamin D synthesis in the skin, there is a variation throughout the world largely due to differences in the geographical latitude. Populations have adapted to the regional intensity of the solar irradiation in different latitudes through evolutionary changes in skin pigmentation [6]. Hence, when compared to the natives of higher latitudes, people in the tropical regions may require longer periods of a direct skin exposure to intense sunlight to generate physiologically required quantities of vitamin D. The problem of insufficient-solar-irradiation-related vitamin D deficiency becomes prominent particularly in darker-skinned migrant populations when they move to higher latitudes [7,8]. This could be translated into a higher risk of developing severe illness if exposed and infected by an influenza virus, which should be taken into consideration when treating critically ill patients.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".