Abstract 297: SPECT/CT Imaging of Regional Foot Perfusion Provides a Quantitative Index for Evaluation of Targeted Revascularization in the Diabetic Foot
Bibliographic record
Abstract
Introduction: No standard quantitative imaging approach exists to evaluate volumetric changes in tissue perfusion in the lower extremities following medical treatment. In this study, we develop and apply a three-dimensional model of the foot for evaluation of regional changes in perfusion following revascularization in diabetic patients with non-healing foot ulcers. We hypothesize that SPECT/CT imaging will permit quantification of regional improvements in tissue perfusion in territories of the foot that contain non-healing ulcers, allowing for quantitative evaluation of revascularization procedures. Methods: Resting 99m Tc-tetrofosmin (dose 554.0 ± 26.6 MBq) SPECT/CT was performed on diabetic patients (n=5; 64 ± 15 yrs) before and 1-3 days after lower extremity angioplasty and/or stenting. The CT attenuation scans were used to define five regions of interest (ROIs) in the foot and for quantification of relative changes in regional perfusion with 99m Tc-tetrofosmin SPECT (Fig. 1A). Radiotracer uptake for each ROI was normalized to injected dose and ROI volume, and expressed as a percent change from baseline value. Results: SPECT/CT imaging demonstrated quantitative improvements in regional tissue perfusion in ROIs containing non-healing ulcers for 4 out of 5 patients following revascularization (Fig. 1B). The single patient demonstrating a negative response (11.7% decrease in perfusion) underwent eventual amputation. Conclusions: Early changes in tissue perfusion following revascularization can be non-invasively evaluated in specific vascular territories of the foot using SPECT/CT imaging and may be associated with wound healing and limb salvage outcomes.
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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 | 0.000 | 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) | 0.005 | 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".