Validation of the Healthy Foot Screen: A Novel Assessment Tool for Common Clinical Abnormalities
Bibliographic record
Abstract
GENERAL PURPOSE: The purpose of this learning activity is to provide information about the Healthy Foot Screen, a new tool for assessment of common foot abnormalities. 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 completing this continuing education activity, you should be able to:1. Recognize prevalence, causes, risk factors, signs, and types of common foot problems.2. Identify the results of this study about the new foot screening tool and its implications in primary care. ABSTRACT: Foot health is a key component of general health and well-being. Nevertheless, feet are often overlooked by healthcare providers and patients. Common foot problems include infections or inflammatory conditions, abnormal nail disorders (eg, onychomycosis), structural bony abnormalities, circulation disorders, and other conditions. The development of an easy-to-use, rapid, clinical tool to assess foot health can facilitate primary care provider recognition and treatment of common foot problems. This study ascertained interrater item reliability and validity from the preliminary version of one such tool called the Healthy Foot Screen.A total of 18 patients from a community dermatology clinic were individually screened by 11 interprofessional healthcare assessors using the preliminary tool. The assessors included a dermatologist/internist, family physicians, nurses, and podiatrists. The initial draft of the Healthy Foot Screen was created through an extensive literature review, complemented by the clinical judgment of the study team. Cronbach α was calculated for each item to determine interrater reliability. A minimum value of 0.6 was set for an item to be included in the final tool. Where applicable, scores for each item on the screen were calculated for right and left lower limbs and then averaged. Assessors were asked to complete a short survey.Interrater reliability scores for items on the screen were as follows: diabetes and smoking, 1.0; neuropathy, 0.988; palpable foot pulse, 0.916; abnormal fourth to fifth toe web space, 0.905; previous ulcer/amputation, 0.869; pitting edema, 0.872; bony abnormality, 0.804; dry bottom of foot, 0.799; toenail infection, 0.793; other spots/lesions,0.688; and red areas/blisters/pustules, 0.659. Generally, assessors found the tool easy to use, although some areas for improvement were noted.The Healthy Foot Screen can facilitate primary care provider diagnosis and treatment of common foot problems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".