Tool for Rapid & Easy Identification of High Risk Diabetic Foot: Validation & Clinical Pilot of the Simplified 60 Second Diabetic Foot Screening Tool
Bibliographic record
Abstract
BACKGROUND: Most diabetic foot amputations are caused by ulcers on the skin of the foot i.e. diabetic foot ulcers. Early identification of patients at high risk for diabetic foot ulcers is crucial. The 'Simplified 60-Second Diabetic Foot Screening Tool' has been designed to rapidly detect high risk diabetic feet, allowing for timely identification and referral of patients needing treatment. This study aimed to determine the clinical performance and inter-rater reliability of 'Simplified 60 Second Diabetic Foot Screening Tool' in order to evaluate its applicability for routine screening. METHODS AND FINDINGS: The tool was independently tested by n=12 assessors with n=18 Guyanese patients with diabetes. Inter-rater reliability was assessed by calculating Cronbach's alpha for each of the assessment items. A minimum value of 0.60 was considered acceptable. Reliability scores of the screening tool assessment items were: 'monofilament test' 0.98; 'active ulcer' 0.97; 'previous amputation' 0.97; 'previous ulcer' 0.97; 'fixed ankle' 0.91; 'deformity' 0.87; 'callus' 0.87; 'absent pulses' 0.87; 'fixed toe' 0.80; 'blisters' 0.77; 'ingrown nail' 0.72; and 'fissures' 0.55. The item 'stiffness in the toe or ankle' was removed as it was observed in only 1.3% of patients. The item 'fissures' was also removed due to low inter-rater reliability. Clinical performance was assessed via a pilot study utilizing the screening tool on n=1,266 patients in an acute care setting in Georgetown, Guyana. In total, 48% of patients either had existing diabetic foot ulcers or were found to be at high risk for developing ulcers. CONCLUSIONS: Clinicians in low and middle income countries such as Guyana can use the Simplified 60-Second Diabetic Screening Tool to facilitate early detection and appropriate treatment of diabetic foot ulcers. Implementation of this screening tool has the potential to decrease diabetes related disability and mortality.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".