Recombinant human platelet‐derived growth factor‐BB (becaplermin) for healing chronic lower extremity diabetic ulcers: an open‐label clinical evaluation of efficacy
Bibliographic record
Abstract
Topically applied recombinant human platelet-derived growth factor-BB (becaplermin) is a new pharmacologically active therapy for chronic, neuropathic, lower extremity diabetic ulcers. In previous studies, becaplermin gel was administered once daily but dressings were changed twice daily. In the present study of 134 patients with diabetes mellitus and full thickness lower extremity ulcers, dressings were changed only once per day, simplifying the treatment regimen. Efficacy criteria included the percentage of patients achieving complete healing within the 20-week treatment period, the time to achieve complete healing, the rate of ulcer recurrence during the 6-month period following healing, and treatment compliance. Complete healing of ulcers was achieved in 57. 5% of patients, with a mean time to closure of 63 days and a recurrence rate of 21% at 6 months. Of the potential factors affecting ulcer healing, only drug compliance (p < 0.001), dressing compliance (p < 0.01), the presence of infection (p < 0.01), baseline ulcer area (p < 0.05), and baseline total wound evaluation score (p < 0.05) were significantly associated with healing. Results of this study further confirm the efficacy and safety of becaplermin gel for the treatment of lower extremity diabetic ulcers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".