Thiamine deficiency disorders: diagnosis, prevalence, and a roadmap for global control programs
Bibliographic record
Abstract
Thiamine is an essential micronutrient that plays a key role in energy metabolism. Many populations worldwide may be at risk of clinical or subclinical thiamine deficiencies, due to famine, reliance on staple crops with low thiamine content, or food preparation practices, such as milling grains and washing milled rice. Clinical manifestations of thiamine deficiency are variable; this, along with the lack of a readily accessible and widely agreed upon biomarker of thiamine status, complicates efforts to diagnose thiamine deficiency and assess its global prevalence. Strategies to identify regions at risk of thiamine deficiency through proxy measures, such as analysis of food balance sheet data and month-specific infant mortality rates, may be valuable for understanding the scope of thiamine deficiency. Urgent public health responses are warranted in high-risk regions, considering the contribution of thiamine deficiency to infant mortality and research suggesting that even subclinical thiamine deficiency in childhood may have lifelong neurodevelopmental consequences. Food fortification and maternal and/or infant thiamine supplementation have proven effective in raising thiamine status and reducing the incidence of infantile beriberi in regions where thiamine deficiency is prevalent, but trial data are limited. Efforts to determine culturally and environmentally appropriate food vehicles for thiamine fortification are ongoing.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".