Accuracy of the pedal acceleration time to diagnose limb ischemia in patients with and without diabetes using the WIfI classification
Bibliographic record
Abstract
INTRODUCTION: Evaluation of limb hemodynamics using the ankle-brachial index (ABI) may be difficult due to skin lesions, extensive necrosis, and obesity, such as commonly present in patients with diabetes with chronic limb-threatening ischemia (CLTI). We hypothesized that the pedal acceleration time (PAT) correlates with ABI and Wound, Ischemia, and foot Infection (WIfI) scores in patients with diabetes to serve as a new modality to accurately stage CLTI. METHODS: A single-center, cross-sectional study included patients with and without diabetes > 18 years with CLTI. Limbs were categorized in three grades of ischemia based on the ABI (ABI < 0.8, < 0.6, and < 0.4) and in two classes based on WIfI stages of amputation risk. Receiver operator characteristic (ROC) curves were used to determine PAT sensitivity, specificity, and accuracy to predict lower-limb ischemia. RESULTS: A total of 141 patients (67 nondiabetic and 74 diabetic) and 198 lower limbs (94 nondiabetic and 104 diabetic) met the inclusion criteria. In patients without diabetes, the accuracy of PAT for detecting an ABI < 0.8 was 85%; for detecting an ABI < 0.6 was 85%; and for detecting an ABI < 0.4 was 87%. In patients with diabetes, the accuracy of PAT in detecting an ABI < 0.8 was 91%; for detecting an ABI < 0.6 was 79%; and for detecting an ABI < 0.4 was 88%. In patients without diabetes, the accuracy for detecting WIfI stages of moderate and high amputation risk was 77% and for patients with diabetes was also 77%. CONCLUSIONS: PAT shows high correlation with the ABI as well as with the WIfI stages of amputation risk and the grades of ischemia, with high accuracy.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".