Cell Therapy, a New Standard in Management of Chronic Critical Limb Ischemia and Foot Ulcer
Bibliographic record
Abstract
Fifty percent of diabetics (7% of general population) suffer from peripheral arterial occlusive disease, which may lead to amputation due to critical limb ischemia (CLI). The aim of our study was to prevent major limb amputation (MLA) in this group of patients using a local application of autologous bone marrow stem cells (ABMSC) concentrate. A total of 96 patients with CLI and foot ulcer (FU) were randomized into groups I and II. Patients in group I (n = 42, 36 males, 6 females, 66.2 ± 10.6 years) underwent local treatment with ABMSC while those in group II (n = 54, control, 42 males, 12 females, 64.1 ± 8.6 years) received standard medical care. The frequency of major limb amputation in groups I and II was 21% and 44% within the 120 days of follow up, respectively (p < 0.05). Only in salvaged limbs of group I both toe pressure and toe brachial index increased (from 22.66 ± 5.32 to 25.63 ± 4.75 mmHg and from 0.14 ± 0.03 to 0.17 ± 0.03, respectively, mean ± SEM). The CD34(+) cell counts in bone marrow concentrate (BMC) decreased (correlation, p = 0.024) with age, even though there was no correlation between age and healing. An unexpected finding was made of relative, bone marrow lymphopenia in the initial bone marrow concentrates in patients who failed ABMSC therapy (21% of MLA). This difference was statistically significant (p < 0.040). We conclude ABMSC therapy results in 79% limb salvage in patients suffering from CLI and FU. In the remaining 21% lymphopenia and thrombocytopenia were identified as potential causative factors, suggesting that at least a partial correction with platelet supplementation may be beneficial.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".