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Record W4385308007 · doi:10.3390/biom13081167

Biological Macromolecule-Based Scaffolds for Urethra Reconstruction

2023· review· en· W4385308007 on OpenAlexaff
Saeed Farzamfar, Mahya Rahmani, Mohammad Naji, Mehdi Aleahmad, Stéphane Chabaud, Stéphane Bolduc

Bibliographic record

VenueBiomolecules · 2023
Typereview
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsTissue engineeringContext (archaeology)UnavailabilityUrethraScaffoldBiomedical engineeringComputer scienceEngineeringMedicineBiologySurgery

Abstract

fetched live from OpenAlex

Urethral reconstruction strategies are limited with many associated drawbacks. In this context, the main challenge is the unavailability of a suitable tissue that can endure urine exposure. However, most of the used tissues in clinical practices are non-specialized grafts that finally fail to prevent urine leakage. Tissue engineering has offered novel solutions to address this dilemma. In this technology, scaffolding biomaterials characteristics are of prime importance. Biological macromolecules are naturally derived polymers that have been extensively studied for various tissue engineering applications. This review discusses the recent advances, applications, and challenges of biological macromolecule-based scaffolds in urethral reconstruction.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.199
GPT teacher head0.415
Teacher spread0.216 · 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

Citations7
Published2023
Admission routes1
Has abstractyes

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