Fermented Dairy Products Consumption and Impact on Nutrients Intake and Nutritional Status by Anthropometric Data in Russian Adults: RLMS‐HSE
Bibliographic record
Abstract
We analyzed the consumption of dairy products and association with the nutritional status of adults based on “Russian Longitudinal Monitoring Survey, RLMS‐HSE” 1994‐2012. Dietary intake (24h recall) and anthropometric data were collected by trained interviewers in >135,000 subjects during observational period. Per capita consumption (PCC) of drinking milk (including milk from prepared meals like porridges, milk soups, etc.) was varying from year to year but did not change critically during the period. PCC of fermented dairies increased by 3 times, while curd/curd products and hard cheeses increased by 2 times and 1.4 time, respectively. Among fermented dairies, kefir increased from 10.9 to 25.6 g/day and yogurt increased from 0.9g to 8.6 g/day. Yogurt consumption decreased with age in adults of both genders with the minimum values in older adults (蠅 60 yo). Higher kefir consumption, on the contrary, was observed in this latter population. In general, yogurt, kefir or the sum of fermented dairies was associated with higher intake of calcium, vitamin B2 and protein. Anthropometric data showed that adult yogurt consumers had lower mean values of BMI (T‐test P<0,001) and lower prevalence of overweight/obesity (OR 0.76, 95% CI 0.68‐0.85, P<0,001) compared to non‐consumers. This was not observed in the group of kefir consumers. Our study showed that fermented dairies consumption is associated with higher nutrients intake. Results further suggested that yogurt consumption is associated with lower prevalence of overweight/obesity among adults.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".