The nutritional status of adult female patients with disabilities in Kuwait
Bibliographic record
Abstract
OBJECTIVES: Adults with disabilities are at a higher risk of malnutrition than are their non-disabled counterparts owing to feeding problems and associated medical conditions. We evaluated the prevalence of malnutrition in a group of institutionalized women and investigated any feeding difficulties and nutrition-related medical problems. METHODS: This study used two versions of the Mini Nutritional Assessment-Short Form (MNA-SF) to screen malnutrition: the MNA-SF1 which uses the body mass index, and the MNA-SF2 which uses the calf circumference. Data were collected from 53 women with intellectual and physical disabilities in a cross-sectional survey of residents of the Kuwait Rehabilitation Centre. RESULTS: Of all participants, 63.5% were found to be overweight or obese, while 11.5% were underweight. Using the MNA-SF1, 57.7% were found to be at risk of malnourishment while 11.5% were malnourished. More patients were identified to be at risk of malnutrition or to be actually malnourished using the MNA-SF2 (59.6% and 23.1%, respectively). Reported feeding problems included difficulties in maintaining a sitting position, manipulating food on a plate, conveying food to the mouth, and in swallowing. The presence of infections worsened the prognoses of malnourished women regardless of their weight status. CONCLUSIONS: Our findings suggest that MNA-SF2 is a more sensitive tool for identifying malnourishment than MNA-SF1. Obesity can obscure the identification of malnourished patients if clinicians rely solely on the MNA-SF1, which uses the body mass index.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".