Validated 60-Second General Foot Screen: A Pilot Trial and Guide to Diagnoses and Treatment
Bibliographic record
Abstract
GENERAL PURPOSE: To provide information on a 60-second General Foot Screen to assist in the prevention and/or identification and management of common foot problems. TARGET AUDIENCE: This continuing education activity is intended for physicians, physician assistants, nurse practitioners, and nurses with an interest in skin and wound care. LEARNING OBJECTIVES/OUTCOMES: After participating in this educational activity, the participant should be better able to:1. Use the 60-second General Foot Screen to assist healthcare professionals in the recognition of common foot problems.2. Identify risk factors, causes, and treatment of selected foot problems. ABSTRACT: Foot health is important to overall patient health. Early diagnosis and treatment of diabetes, neuropathy, fungal foot infections, foot deformity, and vascular disease/lower leg edema can improve patient quality of life. One way to achieve this is effective screening. To this end, researchers piloted a validated 10-item screening tool to assess foot health on 120 patients; 74.17% had at least one positive abnormality, demonstrating the critical importance of these early findings. Only 25.83% of individuals had completely low-risk feet. This easy-to-use tool can assist healthcare professionals in the recognition and treatment of common foot problems. The article also outlines the early signs of disease by screening item and provides a guide to treatment to enable effective prevention and quality care.
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".