Personalized nutrition diagnostics at the point-of-need
Bibliographic record
Abstract
Micronutrient deficiency is widespread and negatively impacts morbidity, mortality, and quality of life globally. On-going advancements in nutritional biomarker discovery are enabling objective and accurate assessment of an individual's micronutrient and broader nutritional status. The vast majority of such assessment however still needs to be conducted in traditional centralized laboratory facilities which are not readily accessible in terms of cost and time in both the developed and developing countries. Lab-on-a-chip (LOC) technologies are enabling an increasing number of biochemical reactions at the point-of-need (PON) settings, and can significantly improve the current predicament in nutrition diagnostics by allowing rapid evaluation of one's nutritional status and providing an easy feedback mechanism for tracking changes in diet or supplementation. We believe that nutrition diagnostics represents a particularly appealing opportunity over other PON applications for two reasons: (1) healthy ranges for many micronutrients are well defined which allows for an unbiased diagnosis, and (2) many deficiencies can be reversed through changes in diet or supplementation before they become severe. In this paper, we provide background on nutritional biomarkers used in nutrition diagnostics and review the emerging technologies that exploit them at the point-of-need.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".