The relevance of food composition data for nutrition surveys in rural Tibet: pilot study in the context of Kashin-Beck Disease
Bibliographic record
Abstract
Kashin-Beck disease (KBD) is an endemic and chronic osteochondropathy. This disease principally occurs in the Tibet Autonomous Region and in several provinces of the People's Republic of China. The etiology of the disease remains obscure although environmental factors are assumed to be involved. Diet, in particular, differentiates the rural community, affected by KBD, from the other communities (nomads and city-dwellers), who remain unaffected. In anticipation of a nutrition survey, this study aimed to measure the mineral content (Ca, P, Mg, Fe, Zn, Mn, Cu, Ni, Se, Al, Sr, Mo, Cd, As, Pb, Hg, Cr, and Co) of eight Tibetan staple foods and to compare the results against two food composition tables (FCTs). Foods were sampled in twenty households selected from both an endemic and a non-endemic area of rural Tibet. Ten minerals involved in bone metabolism were measured using atomic and molecular spectrometric methods. Results revealed that a very limited number of food/constituent pairs showed a variation in mineral composition during a single year of testing for a given region. In addition, results showed significant differences in mineral content between the endemic and the non-endemic area, especially for wheat flour. Following our analysis of the mineral content of the Tibetan food samples, results were statistically compared with similar foods listed in two food composition tables: the USDA National Nutrient Database (USDA Food Search for Windows, Version 1.0, database version SR23), and the China Food Composition Table (book 1, 2nd edition). More than 50 to 60% of p-values < 0.05 were highlighted, suggesting the inappropriateness of using FCTs as a reference for nutrition surveys in rural Tibet, and emphasizing the need for analysis of traditional foods. Differences were found to be more or less marked depending on the element considered, and calcium content seemed to show the greatest difference. Although it is obviously too early for definite conclusions to be reached (insufficient number of samples by food and insufficient number of foods analyzed), it seems that the present pilot-study indicates significant discrepancies between measured and tabulated values of the mineral content of certain foods. A more complete survey would therefore seem mandatory.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".