The hidden impact: frailty and malnutrition in patients with diabetic foot ulcers
Bibliographic record
Abstract
Aims: Diabetic foot ulcers (DFUs) are a significant complication affecting over 30% of individuals with diabetes, leading to increased morbidity and mortality. This study investigates the relationships between frailty, nutritional status, and quality of life in patients aged 50 and older diagnosed with DFUs. Methods: A total of 100 participants with DFUs were prospectively included in the study, with assessments conducted using the Edmonton Frailty Scale and the Mini Nutritional Assessment Scale. Quality of life was evaluated using the EQ-5D-3L scale. Demographic data, concomitant diseases, medications, HbA1c levels, and participants’ height, weight, and circumferences of the upper arm, calf, and waist were recorded. The data analysis was performed using statistical software. Results: The findings revealed that 50% of patients exhibited varying degrees of frailty, and 85% were at risk of malnutrition. Both frailty and malnutrition were associated with a significant decline in quality of life. Notably, patients with normal nutritional status reported higher quality of life scores compared to those at risk of malnutrition or malnourishment. Conclusion: This study underscores the need for a holistic approach to managing DFUs that integrates frailty and nutritional status assessments. Targeted interventions addressing these factors are essential for improving health outcomes and enhancing the quality of life for individuals living with diabetes. The findings advocate a shift from a narrow focus on wound management to a broader, more comprehensive care strategy.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".