Chiropody May Prevent Amputations in Diabetic Patients on Peritoneal Dialysis
Bibliographic record
Abstract
BACKGROUND: A multidisciplinary approach has been shown to be of benefit in the prevention of lower limb ulceration and amputation in patients with diabetes, but there is less information on the role of such an approach in patients receiving dialysis treatment. OBJECTIVE: The purpose of the present study was to determine whether the institution of a chiropody program would result in fewer amputations in diabetic patients on peritoneal dialysis (PD). DESIGN: Retrospective chart review. SETTING: The PD program at a tertiary-care hospital. PATIENTS: Patients with diabetes that were enrolled in the PD program between January 1997 and December 1999, inclusive, that were offered the opportunity to see a chiropodist, and that agreed to be seen. A total of 132 patients were included. INTERVENTION: Education about foot care, assessment, and, in some instances, treatment by a chiropodist. RESULTS: Patients with an amputation were more likely to be male (p < 0.01) and have peripheral vascular disease (p < 0.001) compared to those without an amputation. They also had a lower average mean arterial pressure (p < 0.05), lower weekly creatinine clearance (p < 0.01), higher mean erythropoietin dose (p < 0.05), and longer duration of end-stage renal disease (p < 0.001). Factors that were predictive of shorter time to death or amputation were older age [hazard ratio (HR) = 1.03, p < 0.05], peripheral vascular disease (HR = 2.66, p< 0.01), and cerebrovascular disease (HR = 2.70, p< 0.01). Being seen by a chiropodist was protective (HR = 0.39, p < 0.01). CONCLUSION: The current study suggests that a chiropody program may help to prevent amputation in patients with diabetes on PD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".