Malnutrition and Dysphagia in Long-Term Care: A Systematic Review
Bibliographic record
Abstract
Determining the co-occurrence of malnutrition and dysphagia is important to understand the extent to which swallowing impairment contributes to poor food intake in long-term care (LTC). This review investigated the impact of dysphagia on malnutrition in LTC by synthesizing the results of published literature. Seven electronic databases were used to search for English-language publications reporting malnutrition and dysphagia in LTC facilities from 1946 to 2013. Fourteen studies were eligible for inclusion. Overall, the literature on the co-occurrence of malnutrition and dysphagia in LTC shows a paucity of high-quality evidence. Articles reviewed lacked consistent definitions for both conditions. Methods used to confirm each diagnosis also differed and were of questionable validity. Based on a review of the literature, evidence of the existence of concurrent concerns with respect to malnutrition and dysphagia emerges. The reported frequency of participants in LTC with dysphagia ranges from 7% to 40%, while the percentage of those who were malnourished ranges from 12% to 54%. Due to discrepancies used to describe and measure these conditions, it is difficult to determine the exact prevalence of either condition separately, or in combination. Consequently, the impact of dysphagia on malnutrition must be considered and studied using valid definitions and measures.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".