Clinical and Quality-of-Life Outcomes in Dementia Patients Following Texture-Modified Diets and Thickened Liquids: A Systematic Review
Bibliographic record
Abstract
Background: Dysphagia, impaired swallowing, is one of the most common and life-threatening complications in patients with dementia. It plays a vital role on Aspiration-related lung infection, dehydration, malfunction of nutrition and quality of life. TMDs and thickened liquids are routinely recommended for improving swallowing safety, but evidence supporting their long-term efficacy is inconclusive.Objectives: This study aims to summarize the evidence regarding texture-modified foods and thickened liquids on reducing aspiration or pneumonia among older people with dementia. Secondary effects on hydration, nutrition and psychosocial well-being as well as the application of care are also assessed.Methods: Systematic findings were performed in PubMed, Scopus, Embase, CINAHL Web of Science and Cochrane Library (2000–2025). Both randomized controlled, quasi-experimental, and cohort studies as well as qualitative designs were included if applied TMDs or thickened liquids in older adults (60+) with dementia-related dysphagia. Results were swallowing, aspiration rate pneumonia occurrence hydration nutritional status and qualice of life. The Cochrane Risk-of-Bias 2.0 tool and the Newcastle-Ottawa Scale were used to evaluate methodological quality.Results: Twenty-seven studies were included (8 RCTs, 11 observational, 8 qualitative/mixed-method). Short-term evidence suggests that as the viscosity is increased, fewer aspiration events may be detected on instrumental assessments, in particular with honey-thick (≈3,000 cPs) and nectar-thick (≈300 cPs) fluids. However, the long term outcomes demonstrate bigger pneumonia numbers, increased dehydration and lower compliance with thickened fluids when compared to personal tailored swallowing strategies. There were a small number of occasions where you made texture-modified solid foods, these improved swallow safety; however, for some people they decreased appetite and enjoyment. Safety, compliance, and feeding outcomes were found to improve with the use of person-centred interventions and staff education.Conclusions: Texture-modified foods and liquids can prevent immediate aspiration but not reliably the occurrence of pneumonia. They may also lead to detriments in hydration, nutrition and quality of life if not personalized. Interdisciplinary, evidence-based person-centered care unified with IDDSI standards is critical for quality dementia care.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".