Making the Case for Nutrition Screening in Older Adults in Primary Care
Bibliographic record
Abstract
Many older adults are malnourished and frail; identifying those at risk, specifically in primary care, is a priority. Nutrition screening in physicians' offices, medical clinics, or healthcare centers is one way to identify those at risk who could benefit from treatment. Using the World Health Organization strategies, by Wilson and Junglier (1968) in “Principles and Practice of Screening for Disease,” this article presents the case for why nutrition screening in primary care is a needed change in practice. Specifically, it is recommended that prefrail and/or frail older adults be targeted for nutrition screening to optimize identification and benefits of treatment from referred programs. Evidence exists that this approach is not only necessary but also feasible and practicable.
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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 | 0.036 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.008 | 0.021 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.042 | 0.045 |
| Insufficient payload (model declined to judge) | 0.008 | 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".