<scp>PROtein</scp> enriched <scp>MEDiterranean</scp> diet to combat undernutrition and promote healthy <scp>neuroCOGnitive</scp> ageing in older adults: The <scp>PROMED‐COG</scp> consortium project
Bibliographic record
Abstract
Dementia is a major public health challenge owing to its increasing prevalence and recognised impact on disability among older adults. Observational data indicate that weight loss is associated with increased dementia risk of 30%-40% and precedes a diagnosis of cognitive impairment or dementia by at least one decade. Although relatively little is known about the mechanisms of unintentional weight loss in dementia, this provides a window of opportunity to intervene with strategies to counteract undernutrition and delay, or prevent, the onset of dementia. This article provides an overview of the PROMED-COG project and associated work packages. The project aimes to (1) strengthen the epidemiologic evidence to better understand the potential benefits of combating undernutrition for healthy neurocognitive ageing; (2) increase scientific knowledge on the balance between a protein enriched Mediterranean diet (PROMED) and physical exercise to prevent undernutrition and promote healthy neurocognitive ageing, and generate data on mechanistic pathways; (3) stimulate collaboration and capacity building for nutrition and neurocognitive ageing research in Europe; and (4) develop public and practice recommendations to combat undernutrition and promote healthy neurocognitive ageing in older adults. Findings will provide new and critical insights into the role of undernutrition in neurocognitive ageing, how this role can differ by sex, genetic risk and timing of undernutrition exposure, and how modifications of dietary and physical activity behaviour can reduce the burden of undernutrition and neurodegeneration. The research outcomes will be useful to inform policy and practice about the dietary guidelines of older people and provide insight to industry for the development of food-based solutions to prevent undernutrition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".