Prevalence and Characteristics Associated with Modified Texture Food Use in Long Term Care: An Analysis of Making the Most of Mealtimes (M3) Project
Bibliographic record
Abstract
Purpose: To describe the prevalence and characteristics of modified-texture food (MTF) consumers when applying standard diet terminology. Methods: Making the Most of Mealtimes (M3) is a cross-sectional multi-site study including 32 long-term care (LTC) homes located in 4 Canadian provinces. Resident characteristics were collected from health records using a defined protocol and extraction form. Since homes used 67 different terms to describe MTFs, diets were recategorized using the International Dysphagia Diet Standardization Initiative Framework as a basis for classification. Results: MTFs were prescribed to 47% (n = 298) of participants (n = 639) and prevalence significantly differed among provinces (P < 0.0001). Various resident characteristics were significantly associated with use of MTFs: dysphagia and malnutrition risk, dementia diagnosis, prescription of oral nutritional supplements; lower body weight and calf circumference; greater need for physical assistance with eating; poor oral health status; and dependence in all activities of daily living. Conclusions: This is the first study that used a diverse sample of LTC residents to determine prevalence of MTF use and described consumers. The prevalence of prescribed MTFs was high and diverse across provinces in Canada. Residents prescribed MTFs were more vulnerable than residents on regular texture diets. These findings add value to our understanding of MTF consumers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".