Effects of Nutrition on Osteoarthritis in Women Over the 50 Years: Using the Korea National Health and Nutrition Examination Survey (KNHANES) Data
Bibliographic record
Abstract
There are prior articles on osteoarthritis of demographic and nutritional factors, previous studies show a lack of empirical analysis using public data, focusing primarily on predicting or assessing risk factors focusing on demographic characteristics. Since the disease called osteoarthritis itself has yet to be clearly treated, we would like to establish that the preventive medical aspect of osteoarthritis is very important and that prevention through nutrient intake can reduce the social and economic costs of osteoarthritis. We intend to prepare basic data for osteoarthritis by analyzing the prevalence of osteoarthritis in women over the 50 age based on demographic and nutritional characteristics using the 7th National Nutrition Survey data in 2016 and 2017. As results, cholesterol and sodium negatively affect the odds of osteoarthritis and nutrients classified as inorganic reduce the odds of osteoarthritis in 50 age women to 59 years old. In addition, in women over 60 and under 69 years of age, vitamin B has been found to reduce the risk of osteoarthritis and iron has a significant effect on women in their 70s and older. Therefore, these nutrients are ‘micronutrients’ and, among the five major nutrients, were identified as the nutrients that assisted the ‘macronutrients’.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".