Nutritional Strategies Facing an Older Demographic: The Nutrition UP 65 Study Protocol
Bibliographic record
Abstract
BACKGROUND: The population of Portugal is aging. The lack of data on older adults' nutritional status and the lack of nutrition knowledge amongst health professionals, caregivers, and older adults themselves, remains a challenge. OBJECTIVE: The Nutrition UP 65 study aims to reduce nutritional inequalities in the older Portuguese adult population and improve knowledge regarding older Portuguese adults' nutritional status, specifically relating to undernutrition, obesity, sarcopenia, frailty, hydration, sodium, and vitamin D statuses. METHODS: A representative sample of older Portuguese adults was selected. Sociodemographic, lifestyle, anthropometric, functional, and clinical data were collected. Sodium excretion, hydration, and vitamin D statuses were assessed. RESULTS: Data collection (n=1500) took place between December, 2015 and June, 2016. Results will be disseminated in national and international scientific journals, and via Portuguese media. CONCLUSIONS: Nutrition UP 65 results will provide evidence for the design and implementation of effective preventive public health strategies regarding the elderly. These insights may represent relevant health gains and costs savings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".