Protocol Development of a Personalized Balanced Nutrition Concept for Preschool Children, Primarily Those with Food Allergies, Using an IT Platform
Bibliographic record
Abstract
Children with food allergies are at higher risk for severe anaphylactic reactions and for key nutrient deficiency. In order to address these concerns, enable early detection, and improve the monitoring of children with food allergies, an innovative IT platform will be developed by IT experts (IN2 Ltd. Zagreb, Croatia, part of Constellation Software Inc. (Toronto, ON, Canada)) and Srebrnjak Children's Hospital, Zagreb, Croatia (SCH) for the effective implementation of personalized balanced nutrition in preschool institutions in Croatia. Additionally, the data obtained through this research, including epidemiological data on allergic diseases, clinical data (diagnostic allergy tests and others), anthropometry, and physical activity status, will be used to create a national Allergy registry. Other than being a tool for personalized and balanced nutrition for children, especially those with special dietary requirements (including food allergy and intolerance), the IT platform developed in this study will enable the continuous monitoring of these children as a part of their clinical management plan and earlier detection of food allergies, intolerance, and other conditions, even outside of the healthcare system. This research also aims at optimizing current and developing novel personalized therapeutic regimes, detecting novel early biomarkers in children with food allergies and intolerances, and involving all key stakeholders (caregivers, preschool institutions, etc.) in the shared-care approach in the management of food allergies in children.
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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 | 0.035 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.048 | 0.010 |
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".