Treatment of Diabetic Foot Ulcers Based on an Interdisciplinary Team Approach
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate patients' perception and quality of diabetic foot ulcer (DFU) care delivered by an interdisciplinary team approach (ITA). DESIGN: Exploratory cross-sectional study. SUBJECTS AND SETTING: Twenty patients with a healed plantar DFU were recruited from an interdisciplinary Wound Care clinic of a Canadian University affiliated hospital. Their mean age was 64 years (75% were males [n = 15]), 18 (90%) were living with type 2 diabetes, and 45% (n = 9) had osteomyelitis in the previous year of their enrollment in the study. METHODS: The validated short form of the Quality From the Patient's Perspective questionnaire was used to evaluate quality of care dimensions (medical-technical competence of the caregivers; physical-technical conditions of the care organization; degree of identity-orientation in the attitudes and actions of the caregivers; and sociocultural atmosphere of the care organization). RESULTS: Respondents reported experiencing a high level of quality care with an ITA. All indicators of patient-perceived reality of care delivered were superior or equal related to their subjective importance in all dimensions of quality care (with scores ranging from 3.85 to 4.00 on a 4-Point Likert scale). Patients' satisfaction regarding the ITA was high. CONCLUSIONS: Study findings suggest that an ITA model provided high quality of care for treating DFUs for all quality dimensions judged important for patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".