Tissue-Engineered Wound Dressings for Diabetic Foot Ulcers
Bibliographic record
Abstract
As the prevalence of patients with diabetes is rising, the number of patients at risk of developing diabetic foot ulcers (DFUs) is increasing. DFU is one of the most debilitating complications of diabetes because of the high risk of infections and lower extremity amputations. The standard of care for DFU treatment is limited to non-specific diagnostics (wound size, depth) and therapeutic interventions (debridement, wet gauze, antibiotics if needed), and has moderate healing rates. In the past three decades, a number of FDA-approved therapies have been developed that take advantage of advances in biomaterials and tissue engineering to manufacture materials that provide a favorable physicochemical environment and pro-healing cues (e.g., extracellular matrix, growth factors). However, these treatments have not led to strongly increased healing rates. As our understanding of diabetic wound pathophysiology is deepening, new therapeutic targets have emerged, particularly around inflammatory processes in diabetic wounds. In the last decade, the emergence of these targets has led to the development of wound dressings that release diagnostic and therapeutic agents that specifically address these processes. While many of these therapies have yet to provide a clinical proof-of-concept, the variety of strategies raises hope that novel and more specific diagnostic and therapeutic options will be available in the next decade.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".