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Biomechanical Response under Stress-Controlled Tension-Tension Fatigue of a Novel Carbon Fiber/Epoxy Intramedullary Nail for Femur Fractures

2020· article· en· W3022690572 on OpenAlexafffund
Saeid Samiezadeh, Emil H. Schemitsch, Radovan Zdero, Habiba Bougherara

Bibliographic record

VenueMedical Engineering & Physics · 2020
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsWestern UniversityToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIntramedullary rodTension (geology)EpoxyMaterials scienceFemurComposite materialStress (linguistics)Nail (fastener)FiberOrthodonticsSurgeryMedicineCompression (physics)

Abstract

fetched live from OpenAlex

Metallic intramedullary nails are the “gold standard” implant for repairing femur shaft fractures. However, their rigidity may eliminate axial micromotion at the fracture (causing delayed healing) and they may carry too much load relative to the femur (causing “stress shielding”). Consequently, some researchers have proposed fiber-reinforced composite nails, but only one evaluated cyclic fatigue performance. Therefore, this study assessed the cyclic fatigue response of a carbon fiber/epoxy nail with a novel ply stacking sequence of [02/-45/452/-45/0/-45/452/-452/452/-45/902] previously developed by the present authors. Nails were cyclically loaded in tension-tension at 5 Hz with a stress ratio of R=0.1 from 30% - 85% of the material's ultimate tensile strength (UTS). Thermographic stress analysis, rather than conventional fatigue testing, was used to obtain high cycle fatigue strength (HCFS), below which the nail can be cyclically loaded indefinitely without damage. Also, the mechanical test machine's built-in load cell and an extensometer were used to create stress-strain curves, from which the change in static EO and dynamic E* moduli were obtained. Results showed that HCFS was 70.3% of UTS (or about 283 MPa), while EO and E* remained at 42 GPa without any dRegradation during testing. The current nail shows potential for clinical use.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.024
GPT teacher head0.283
Teacher spread0.259 · 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 designSimulation or modeling
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

Citations12
Published2020
Admission routes2
Has abstractyes

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