Modified Texture Food Use is Associated with Malnutrition in Long Term Care: An Analysis of Making the Most of Mealtimes (M3) Project
Bibliographic record
Abstract
OBJECTIVE: Modified texture food (MTF), especially pureed is associated with a high prevalence of under-nutrition and weight loss among older adults in long term care (LTC); however, this may be confounded by other factors such as dependence in eating. This study examined if the prescription of MTF as compared to regular texture food is associated with malnutrition risk in residents of LTC homes when diverse relevant resident and home-level covariates are considered. DESIGN: Making the Most of Mealtimes (M3) is a cross-sectional multi-site study. SETTING: 32 LTC homes in four Canadian provinces. PARTICIPANTS: Regular (n= 337) and modified texture food consumers (minced n= 139; pureed n= 68). MEASUREMENTS: Malnutrition risk was determined using the Mini Nutritional Assessment short-form (MNA-SF) score. The use of MTFs, and resident and site characteristics were identified from health records, observations, and standardized assessments. Hierarchical linear regression analyses, accounting for clustering, were performed to determine if the prescription of MTFs is associated with malnutrition risk while controlling for important covariates, such as eating assistance. RESULTS: Prescription of minced food [F(1, 382)=5.01, p=0.03], as well as pureed food [F(1, 279)=4.95, p=0.03], were both significantly associated with malnutrition risk among residents. After adjusting for age and sex, other significant covariates were: use of oral nutritional supplements, eating challenges (e.g., spitting food out of mouth), poor oral health, and cognitive impairment. CONCLUSIONS: Prescription of minced or pureed foods was significantly associated with the risk of malnutrition among residents living in LTC facilities while adjusting for other covariates. Further work needs to consider improving the nutrient density and sensory appeal of MTFs and target modifiable covariates.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".