The Evidence for Screening Older Adults for Nutrition Risk in Primary Care: An Umbrella Review
Bibliographic record
Abstract
It is not known if nutrition risk screening of older adults should be a standard practice in primary care. The evidence in support of nutrition risk screening of older adults in primary care was examined and critically analyzed using an umbrella review. The peer reviewed and grey literature were searched for clinical practice guidelines (CPGs) and systematic reviews (SRs). Titles and abstracts were independently screened by the two authors. Resources were excluded if they did not apply to older adults, did not discuss nutrition/malnutrition risk screening, or were in settings other than primary care. Full texts were independently screened by both authors, resulting in the identification of six CPGs and three SRs that met the review criteria. Guidelines were appraised with the AGREE II tool and SRs with the AMSTAR 2 tool. The quality of the CPGs was high, while the quality of the SRs was low. The CPGs and SRs acknowledged a lack of high-quality research on the benefits of regular nutrition risk screening for older adults in primary care; however, CPGs recommended annual screening for older adults in primary care practices or other community settings. High-quality research investigating nutrition risk screening of older adults in primary care is needed.
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 | 0.024 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".