MétaCan
Menu
Back to cohort
Record W4309668385 · doi:10.3390/jfb13040249

Collagen as a Biomaterial for Skin and Corneal Wound Healing

2022· review· en· W4309668385 on OpenAlexafffund
Renáta Sklenářová, Naoufal Akla, Meagan Jade Latorre, Jitka Ulrichová, Jana Franková

Bibliographic record

VenueJournal of Functional Biomaterials · 2022
Typereview
Languageen
FieldMedicine
TopicWound Healing and Treatments
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
FundersUniverzita Palackého v OlomouciUniversité de Montréal
KeywordsWound healingExtracellular matrixMyofibroblastBiomaterialConnective tissueSelf-healing hydrogelsBiomedical engineeringMaterials scienceCorneaTissue engineeringSkin repairRegeneration (biology)Cell biologyPathologyMedicineFibrosisSurgeryBiologyOphthalmology

Abstract

fetched live from OpenAlex

The cornea and the skin are two organs that form the outer barrier of the human body. When either is injured (e.g., from surgery, physical trauma, or chemical burns), wound healing is initiated to restore integrity. Many cells are activated during wound healing. In particular, fibroblasts that are stimulated often transition into repair fibroblasts or myofibroblasts that synthesize extracellular matrix (ECM) components into the wound area. Control of wound ECM deposition is critical, as a disorganized ECM can block restoration of function. One of the most abundant structural proteins in the mammalian ECM is collagen. Collagen type I is the main component in connective tissues. It can be readily obtained and purified, and short analogs have also been developed for tissue engineering applications, including modulating the wound healing response. This review discusses the effect of several current collagen implants on the stimulation of corneal and skin wound healing. These range from collagen sponges and hydrogels to films and membranes.

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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.129
GPT teacher head0.390
Teacher spread0.261 · 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

Citations74
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Functional BiomaterialsSame topicWound Healing and TreatmentsFrench-language works237,207