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Record W4381162795 · doi:10.1055/s-0043-1768990

Effects of Transfixation Pin Positioning on the Biomechanical Properties of Acrylic External Skeletal Fixators in a Fracture Gap Model

2023· article· en· W4381162795 on OpenAlexaff
Joachim Lahiani, Mila Freire, Xavier Montasell, Tristan Juette, Dominique Gagnon

Bibliographic record

VenueVeterinary and Comparative Orthopaedics and Traumatology · 2023
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBendingStiffnessCompression (physics)Structural engineeringEccentricBending stiffnessThree point flexural testComposite materialYield (engineering)Materials scienceFracture (geology)OrthodonticsMedicineEngineering

Abstract

fetched live from OpenAlex

Abstract Objective The aim of this study was to evaluate the biomechanical effects of transfixation pin positioning in acrylic columns of external skeletal fixators (ESF). Study Design Twenty-four type I acrylic ESF were built simulating a fracture gap-model. Transfixation pins were placed centric (n = 12) or eccentric at ¼ of the column diameter (n = 12) in the acrylic columns. Six constructs from each group were subjected to axial compression and four-point bending tests. Stiffness, yield load and mode of failure were recorded. Results Stiffness was not influenced by centring (p = 0.373), but it was higher in four-point bending than in axial compression (p < 0.001). Pin positioning had no influence on the yield (p = 0.535) and failure loads (p = 0.715) in axial compression, nor on the yield load in bending (p = 0.135). Eccentric pin positioning decreased failure loads by 28% in bending (p < 0.001). Conclusion Eccentric position of transfixation pins within the acrylic columns alters the biomechanical properties of type I ESF constructs. While acrylic offers several advantages, when forming the columns, frame strength will be optimized if pins are centrally located.

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 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 score0.598
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.074
GPT teacher head0.307
Teacher spread0.233 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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