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Record W2026183588 · doi:10.1097/bot.0b013e31817e49d1

The Effect of Cement Mixing Time on the Biomechanics of Cement Augmented Plated Fractures in Canine Femora

2008· article· en· W2026183588 on OpenAlexaff
Chris H. Gallimore, Alison J McConnell, Rad Zdero, Henry Koo, Michael D. McKee, Emil H. Schemitsch

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2008
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsCementCadaveric spasmBiomechanicsStiffnessMedicineComposite materialFixation (population genetics)BendingFracture (geology)Bone cementCadaverOrthodonticsMaterials scienceSurgeryAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVE: The goal of this study was to determine the effect of cement mixing time and, hence, cement viscosity on the biomechanical behavior of femoral fracture fixation. DESIGN: Cadaveric plated canine femoral fracture model, comparing treatments in matched pairs. SETTING: Orthopaedic biomechanics laboratory. INTERVENTION: Cement was inserted both as a liquid and as a paste in standard and oversized screw holes to augment fixation with plates and screws. MAIN OUTCOME MEASUREMENTS: Standard 4-point bending tests were performed to obtain stiffness and failure load values. RESULTS: Liquid cement had a 1.38 times increase in stiffness and a failure load 1.84 times greater compared with paste cement, regardless of hole size with a gap at the fracture site (P < 0.05). Liquid cement had a force to failure of 1.77 and 1.91 times in the standard-sized and oversized holes, respectively, when compared with paste cement (P < 0.05). CONCLUSIONS: When the cement was inserted in a liquid state in a plated femoral diaphyseal fracture with a gap, screw purchase augmentation achieved greater bending stiffness and resisted a greater failure load.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.346
Threshold uncertainty score0.376

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 head0.014
GPT teacher head0.266
Teacher spread0.251 · 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 designObservational
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

Citations4
Published2008
Admission routes1
Has abstractyes

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