Estimation of toe brachial index based on forefoot Doppler waveforms
Bibliographic record
Abstract
Abstract Aims The toe brachial index (TBI) is a standard diagnostic tool for assessing distal perfusion in peripheral arterial disease (PAD) but has several limitations. Doppler waveform characteristics of forefoot arteries, such as acceleration index (AccI), peak systolic velocity (PSV), and acceleration time (AT), present a potentially reliable and more accessible alternative for estimating TBI. This study evaluated the association between Doppler waveform characteristics and standard TBI, developed empirical equations for estimating TBI (eTBI), and assessed their accuracy, reproducibility, and clinical applicability. Methods and results This study presents a prospective analysis of angle-corrected Doppler AccI, PSV, and AT in forefoot metatarsal arteries together with standard automated TBI in 155 limbs of PAD patients treated at Surrey and Sussex Healthcare NHS Trust, Redhill, UK. Doppler-derived AccI, PSV, and AT were significantly associated with standard TBI (R2 = 0.88, 0.58, 0.62; each P < 0.001). Empirical equations for eTBI calculation demonstrated excellent agreement with standard TBI, with minimal average deviations [−0.01 ± 0.10 (SD) for AccI]. Multivariable analysis confirmed that eTBI derived from AccI predicted TBI largely independent of age, sex, diabetes mellitus, Fontaine stage, diastolic blood pressure, and kidney function (R2 = 0.89). After revascularization, both eTBI and standard TBI increased significantly, with strong correlation (r = 0.95, P < 0.001). Inter- and intra-observer and inter-device variability for eTBI measurements was low, outperforming standard TBI. Conclusion Doppler waveform-derived eTBI, particularly using AccI, provides a reproducible, accurate, and clinically responsive alternative to standard TBI. These findings support its integration into routine vascular diagnostics, enhancing accessibility and diagnostic precision in PAD care. Automated eTBI acquisition could enhance screening efficiency in non-specialist settings.
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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 | 0.005 | 0.002 |
| 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".