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Record W1860587198 · doi:10.1111/wrr.12050

The use of a polylactide‐based copolymer as a temporary skin substitute in deep dermal burns: 1‐year follow‐up results of a prospective clinical noninferiority trial

2013· article· en· W1860587198 on OpenAlexaboutno aff
Harald Selig, Maike Keck, David B. Lumenta, Martina Mittlböck, Lars‐Peter Kamolz

Bibliographic record

VenueWound Repair and Regeneration · 2013
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryVascularityArtificial skinDermatology

Abstract

fetched live from OpenAlex

Deep dermal burns can be covered with different kind of materials and techniques; one of them is a polylactide-based temporary skin substitute. The aim of this study was to intraindividually compare its 1-year outcome with the results obtained by use of autologous skin grafts in patients suffering from deep dermal burns. A prospective noninferiority trial was designed in order to assess skin quality and scar formation by use of subjective (Vancouver Scar Scale; Patient and Observer Scar Assessment Scale) and objective (noninvasive cutometry) burn scar assessment tools. All items of the Patient and Observer Scar Assessment Scale, except vascularity, were found to be noninferior in the areas covered with the temporary skin substitute vs. autologous skin. Results of objective scar evaluation showed comparable viscoelastic parameters without reaching noninferiority. Overall, the outcome of deep dermal burns covered with a polylactide-based temporary skin substitute revealed satisfactory results in terms of scar formation and skin quality as compared with autologous skin. This paper supports its use in deep dermal burns, where autologous skin donor sites require either to be reserved for coverage of full-thickness skin defects in severe burns or to be saved for reduction of additional morbidity in selected patient collectives.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.054
GPT teacher head0.333
Teacher spread0.279 · 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

Citations16
Published2013
Admission routes1
Has abstractyes

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