Guidelines for Diabetic Foot Care: Recommendations Endorsed by the Diabetes Committee of the American Orthopaedic Foot and Ankle Society
Bibliographic record
Abstract
Foot infection is the most common reason for hospital admission of diabetic patients in the United States. Foot ulceration leads to deep infection, sepsis, and lower extremity amputation. Prophylactic foot care has been shown to decrease patient morbidity, decrease the utilization of expensive resources, and decrease the risk for amputation and premature death. The Diabetes Committee of the American Orthopaedic Foot and Ankle Society has developed guidelines for the implementation of this type of prophylactic foot care. The screening examination includes evaluation for peripheral neuropathy, skin integrity, ulcers or wounds, deformity, vascular insufficiency, and footwear. Foot-specific patient education includes instruction on self-examination and foot care practices. Individualized foot-specific patient education is indicated for patients with peripheral neuropathy. Treatment is outlined based on risk level, which is determined by the presence of peripheral neuropathy, deformity, and ulcer history. Treatment combines patient education, orthoses, footwear, and a timetable for ongoing skin and nail care. Ulcer care includes paring of calluses, debridement of infected or nonviable tissue, dressings, and off-loading. Specialty assistance may be required from a vascular surgeon, orthopaedic surgeon, podiatrist, endocrinologist/diabetologist, infectious disease consultant, radiologist, and pedorthist.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".