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Record W4283387039 · doi:10.1002/jbm.b.35113

Composition, host responses and clinical applications of bioadhesives

2022· review· en· W4283387039 on OpenAlexafffund
Vignesh Dhandapani, Vickie Ringuette, Monika Desrochers, Marco Sirois, Patrick Vermette

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2022
Typereview
Languageen
FieldMedicine
TopicSurgical Sutures and Adhesives
Canadian institutionsCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolyethylene glycolBiomedical engineeringCyanoacrylateFibrin glueHemostatic AgentBioadhesiveFibrinGlutaraldehydeChemistryMedicineHemostasisAdhesiveMaterials scienceSurgeryNanotechnologyDrug deliveryChromatographyBiochemistry

Abstract

fetched live from OpenAlex

Bioadhesives are medical devices used to join or seal tissues that have been injured or incised. They have been classified into tissue adhesives, sealants, and hemostatic agents. Bioadhesives such as FloSeal®, CoSeal®, BioGlue®, Evicel®, Tisseel®, Progel™ PALS, and TissuGlu® have been commercialized and used in clinical setting. They can be formulated with natural or synthetic components or a combination of both including albumin, glutaraldehyde, chitosan, cyanoacrylate, fibrin and thrombin, gelatin, polyethylene glycol (PEG), along with urethanes. Each formulation has intrinsic properties and has been developed and validated for a specific application. This review article briefs the mechanisms by which bioadhesives forms adhesion to tissues and highlights the correlation between bioadhesives composition and their potential host responses. Furthermore, clinical applications of bioadhesives and their application-driven requirements are outlined.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0010.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.194
GPT teacher head0.493
Teacher spread0.299 · 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 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

Citations22
Published2022
Admission routes2
Has abstractyes

Explore more

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