Texture-Modified Diet for Improving the Management of Oropharyngeal Dysphagia in Nursing Home Residents: An Expert Review
Bibliographic record
Abstract
OBJECTIVES: This paper provides evidence-based and, when appropriate, expert reviewed recommendations for long-stay residents who are prescribed texture-modified diets (TMDs), with the consideration that these residents are at high risk of worsening oropharyngeal dysphagia (OD), malnutrition, dehydration, aspiration pneumonia, and OD-associated mortality, poorer quality of life and high costs. DESIGN: Nestlé Health Science funded an initial virtual meeting attended by all authors, in which the unmet needs and subsequent recommendations for OD management were discussed. The opinions, results, and recommendations detailed in this paper are those of the authors, and are independent of funding sources. SETTING: OD is common in nursing home (NH) residents, and is defined as the inability to initiate and perform safe swallowing. The long-stay NH resident population has specific characteristics marked by a shorter life expectancy relative to community-dwelling older adults, high prevalence of multimorbidity with a high rate of complications, dementia, frailty, disability, and often polypharmacy. As a result, OD is associated with malnutrition, dehydration, aspiration pneumonia, functional decline, and death. Complications of OD can potentially be prevented with the use of TMDs. RESULTS: This report presents expert opinion and evidence-informed recommendations for best practice on the nutritional management of OD. It aims to highlight the practice gaps between the evidence-based management of OD and real-world patterns, including inadequate dietary provision and insufficient staff training. In addition, the unmet need for OD screening and improvements in therapeutic diets are explored and discussed. CONCLUSION: There is currently limited empirical evidence to guide practice in OD management. Given the complex and heterogeneous population of long-stay NH residents, some 'best practice' approaches and interventions require extensive efficacy testing before further changes in policy can be implemented.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".