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Record W2968320473 · doi:10.1016/j.burns.2019.07.021

An open-label, prospective, randomized, controlled, multicenter, phase 1b study of StrataGraft skin tissue versus autografting in patients with deep partial-thickness thermal burns

2019· article· en· W2968320473 on OpenAlexfundno aff
James H. Holmes, Michael J. Schurr, Booker King, Kevin N Foster, Lee D. Faucher, Mary A. Lokuta, Allen R. Comer, Peggy J. Rooney, Kelly F. Barbeau, Stuart T. Mohoney, Angela Gibson, B. Lynn Allen‐Hoffmann

Bibliographic record

VenueBurns · 2019
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
FundersArmed Forces Institute of Regenerative MedicineMallinckrodt PharmaceuticalsWake Forest University
KeywordsMedicineSurgeryCosmesisTolerabilityAdverse effectRandomized controlled trialCohortWound healingTotal body surface areaProspective cohort studyThermal burnInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: This open-label, controlled, randomized study assessed the safety, tolerability, and efficacy of StrataGraft tissue compared to autograft in the treatment of deep partial-thickness (DPT) burns. METHODS: cryopreserved tissue. On each subject, two comparable areas of DPT burn were randomized to receive StrataGraft tissue or autograft. Coprimary end points were the percent area of the StrataGraft tissue treatment site undergoing salvage autografting by Day 28 and wound closure of treatment sites by 3 months. RESULTS: By Day 28, no StrataGraft tissue treatment sites underwent autografting. By 3 months, 93% and 100% of the StrataGraft tissue and autograft treatment sites achieved complete wound closure, respectively. No significant differences in observer total and overall opinion POSAS scores between StrataGraft tissue and autograft treatment sites were observed at any timepoint. The most common adverse event was pruritus (17%). CONCLUSIONS: StrataGraft tissue treatment of DPT thermal burns reduced the need for autograft, resulted in wound closure and treatment-site cosmesis comparable to that of autograft, and was well tolerated.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.020
GPT teacher head0.343
Teacher spread0.324 · 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 designRandomized trial
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

Citations31
Published2019
Admission routes1
Has abstractyes

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