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Record W2979874716 · doi:10.1042/bio02901004

Tissueengineered skin and the treatment of burns: Skin doctor

2007· article· en· W2979874716 on OpenAlexaff
François Berthod

Bibliographic record

VenueThe Biochemist · 2007
Typearticle
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBench to bedsideMedicineSkin careArtificial skinSevere burnCover (algebra)Intensive care medicineSpongeSurgeryDermatologyMedical physicsBiologyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

The increasing survival rates of extensively burnt patients due to the progress in intensive care has amplified the need to find alternative strategies to cover these deep burns. Tissueengineered skin models have been developed; culturing keratinocytes over a collagen gel or sponge populated with fibroblasts. Some of these models have been commercialized as biomaterials or highly sophisticated wound dressings, demonstrating the feasibility of tissueengineered skin development from bench to bedside. Investigations now aim to optimize these models to overcome some limitations encoun tered in their clinical applications, such as their vascularization and innervation.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.012
GPT teacher head0.290
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2007
Admission routes1
Has abstractyes

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