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Defining success in clinical trials of diabetic foot wounds: the Los Angeles DFCon consensus

2009· article· en· W1975619024 on OpenAlexaff
David G. Armstrong, Andrew J.M. Boulton, George Andros, Christopher E. Attinger, David E. Eisenbud, Lawrence A. Lavery, Benjamin A. Lipsky, Joseph L. Mills, Gary Sibbald, Adrianne P. S. Smith, Dane K. Wukich, David J. Margolis

Bibliographic record

VenueInternational Wound Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineDiabetic footClinical trialFoot (prosody)Diabetes mellitusSurgeryIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.070
GPT teacher head0.427
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2009
Admission routes1
Has abstractyes

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