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Record W2169819914 · doi:10.3415/vcot-13-06-0084

Biomechanical testing of a hybrid locking plate fixation of equine sesamoid osteotomies

2014· article· en· W2169819914 on OpenAlexafffund
A. Levasseur, M. Lacourt, Y Elce, Yvan Petit, E. Almeida da Silveira

Bibliographic record

VenueVeterinary and Comparative Orthopaedics and Traumatology · 2014
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Equine Medical Research
Canadian institutionsL'Alliance Boviteq
FundersUniversité de Montréal
KeywordsMedicineSesamoid boneFixation (population genetics)OrthodonticsSurgeryRadiography

Abstract

fetched live from OpenAlex

OBJECTIVE: To compare the biomechanical properties of a hybrid locking compression plate (LCP) construct with the compression screw technique as a treatment for transverse mid-body proximal sesamoid bone fractures. METHODS: Ten paired forelimbs from abattoir horses were used. The medial proximal sesamoid bone of each limb was osteotomized transversely and randomly assigned, to either repair with a two-hole 3.5 mm LCP or a 4.5 mm cortical screw placed in lag fashion. Each limb was tested biomechanically by axial loading in single cycle until failure. The point of failure was evaluated from the load-displacement curves. Then a gross evaluation and radiographs were performed to identify the mode of failure. RESULTS: The loads to failure of limbs repaired with the hybrid LCP construct (4968 N ± 2167) and the limbs repaired with the screw technique (3009 N ± 1091) were significantly different (p <0.01). The most common mode of failure was through a comminuted fracture of the apical fragment of the proximal sesamoid bone. CLINICAL SIGNIFICANCE: The LCP technique has potential to achieve a better fracture stability and healing when applied to mid-body fractures of the proximal sesamoid bone. Further testing, particularly fatigue resistance is required to corroborate its potential as a treatment option for mid-body fractures of the proximal sesamoid bone.

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 score0.767
Threshold uncertainty score0.878

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 studies0.0000.001
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 head0.222
GPT teacher head0.382
Teacher spread0.161 · 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

Citations2
Published2014
Admission routes2
Has abstractyes

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