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Record W2187450966 · doi:10.3390/ma8125453

Physical Characteristics of Medical Textile Prostheses Designed for Hernia Repair: A Comprehensive Analysis of Select Commercial Devices

2015· article· en· W2187450966 on OpenAlexafffund
Miao Linli, Fang Wang, Lu Wang, Ting Zou, Gaétan Brochu, Robert Guidoin

Bibliographic record

VenueMaterials · 2015
Typearticle
Languageen
FieldMedicine
TopicHernia repair and management
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
FundersHigher Education Discipline Innovation ProjectUniversité Laval
KeywordsPorosityPolygon meshMaterials scienceVoid (composites)Fabric structureTextileComputer scienceBiomedical engineeringComposite materialEngineeringComputer graphics (images)

Abstract

fetched live from OpenAlex

Inguinal hernia repairs are among the most frequent operations performed worldwide. This study aims to provide further understanding of structural characteristics of hernia prostheses, and better comprehensive evaluation. Weight, porosity, pore size and other physical characteristics were evaluated; warp knitting structures were thoroughly discussed. Two methods referring to ISO 7198:1998, i.e., weight method and area method, were employed to calculate porosity. Porosity ranged from 37.3% to 69.7% measured by the area method, and 81.1% to 89.6% by the weight method. Devices with two-guide bar structures displayed both higher porosity (57.7%–69.7%) and effective porosity (30.8%–60.1%) than single-guide bar structure (37.3%–62.4% and 0%–5.9%, respectively). Filament diameter, stitch density and loop structure combined determined the thickness, weight and characteristics of pores. They must be well designed to avoid zero effective porosity regarding a single-bar structure. The area method was more effective in characterizing flat sheet meshes while the weight method was perhaps more accurate in describing stereoscopic void space for 3D structure devices. This article will give instructive clues for engineers to improve mesh structures, and better understanding of warp knitting meshes for surgeons.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.058
GPT teacher head0.352
Teacher spread0.293 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations32
Published2015
Admission routes2
Has abstractyes

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