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Record W6990042188

A comparative experimental trial between two artificial skin substitutes: Integra bilayer versus Matriderm 2mm to cover a full-thickness skin wound

2017· dissertation· en· W6990042188 on OpenAlexaboutno aff

Bibliographic record

VenueRepositori UJI (Universitat Jaume I) · 2017
Typedissertation
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsArtificial skinWound healingSkin graftingCover (algebra)ScaffoldHuman skinWound care
DOInot available

Abstract

fetched live from OpenAlex

Background: Artificial Skin Substitutes (ASS) are bioengineered materials designed as acellular scaffolds that act as substrate for cellular growth, proliferation and support for new tissue formation. ASS are indicated as alternatives to wound healing in circumstances when standard therapies are not feasible. ASS are a reality since 1981 and there currently are a huge range of matrix to choose. Integra® has been the first one and the most used worldwide. Nevertheless, there are other ASS with different characteristics than can improve it. Matriderm® 2mm is a new acellular scaffold similar to Integra®, but with a different material composition that allows an earlier cover of the skin loss with a skin autograft.\nObjective: To compare the clinical outcomes between Matriderm® 2 mm versus Integra® bilayer, as a treatment to cover a full-thickness skin wound, using the clinical scales Vancouver Scar Scale (VSS) and Patient and Observes Scar Assessment Scale (POSAS).\nIntervention: Both ASS will be performed in two-step grafting procedure. The wound will be covered at first with an ASS and there will be a second stage surgery to graft a split-thickness skin autograft.\nMethods/Design: It is designed a multicenter, controlled, randomized and single blind trial. 154 patients with full-thickness skin wound that fulfill the inclusion and exclusion criteria will be included

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 categoriesMeta-epidemiology (narrow)
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 score0.354
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.076
GPT teacher head0.392
Teacher spread0.316 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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