The My Meal Intake Tool (M-MIT): Validity of a Patient Self-Assessment for Food and Fluid Intake at a Single Meal
Bibliographic record
Abstract
OBJECTIVES: Hospital malnutrition is an under-recognized issue that leads to a variety of adverse outcomes, especially for older adults. Food/fluid intake (FFI) monitoring in hospital can be used to identify those who are improving and those who need further treatment. Current monitoring practices such as calorie counts are impractical for all patients and a patient-completed tool, if valid, could support routine FFI monitoring. The aim of this research was to determine whether the patient-completed My Meal Intake Tool (M-MIT) can accurately represent FFI at a single meal. DESIGN: Cross-sectional, multi-site. SETTING: Four acute care hospitals in Canada. PARTICIPANTS: 120 patients (65+ yrs, adequate cognition). MEASUREMENTS: Participants completed M-MIT for a single meal. Food and fluid waste was visually estimated by a research dietitian at each hospital. Sensitivity (Se), specificity (Sp) and overall agreement were calculated for both food and fluid intake by comparing M-MIT and dietitian estimations to determine criterion validity of M-MIT. Patient and research dietitian comments were used to make revisions to the M-MIT. RESULTS: Using a cut-point of ≤50% intake, Se was 76.2% and 61.9% and Sp was 74.0% and 80.5% for solid and fluids respectively (p<0.001). M-MIT identified a greater proportion of participants (37.2%) as having low FFI (≤50%) than dietitians (25.0%), as well as a greater proportion identified with low fluid intake (28.3% vs. 24.6%). Modest revisions were made to improve the tool. CONCLUSION: This study has demonstrated initial validity of M-MIT for use in older patients with adequate cognition. Use of M-MIT could promote FFI monitoring as a routine practice to make clinical decisions about care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".