Defining success in clinical trials of diabetic foot wounds: the Los Angeles DFCon consensus
Bibliographic record
Abstract
Regulatory requirements for new products should be guided by clinical trials that protect the public by a thorough evaluation of safety and efficacy, while not creating unnecessary barriers to their development and ultimate approval. While healing a wound is the ultimate goal of treating an individual with a diabetic foot ulcer, achieving this goal is physiologically complex requiring the initiation and interaction of many events and therefore unlikely to be achieved by one compound. We believe that developing new, more meaningful, study outcomes or end points in wound care trials would both aid in determining the true efficacy of wound management modalities and facilitate the product development cycle. The primary guidance from the US Food and Drug Administration to industry in this field was published in 2006. This document, while helpful and largely in concert with current knowledge of wound healing, needs to be substantially improved. We therefore convened an interdisciplinary task force comprising experts in various aspects of wound care to attempt to achieve consensus in defining primary outcomes and potential secondary endpoints for various classes of wound-healing modalities.
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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 | 0.534 | 0.353 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.021 | 0.021 |
| Open science | 0.023 | 0.012 |
| Research integrity | 0.044 | 0.046 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".