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Record W2036277570 · doi:10.1109/embc.2014.6944587

Estimation of pedicle screw fixation strength from probe indentation force and screw insertion torque: A biomechanical study on bone surrogates of various densities

2014· article· en· W2036277570 on OpenAlexafffund
Hedayeh Mehmanparast, Jean‐Marc Mac‐Thiong, Yvan Petit

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsUniversité de MontréalÉcole de Technologie Supérieure
FundersCanada Research Chairs
KeywordsIndentationTorqueFixation (population genetics)Materials scienceBiomechanicsOrthodonticsBiomedical engineeringComposite materialEngineeringAnatomyMedicinePhysics

Abstract

fetched live from OpenAlex

Posterior pedicle screw fixation is commonly used for patients with spinal disorders. However, failure of fixation is reported in many cases and surgeons have only little information. The objective of this study was to assess the correlation between the probe indentation force, screw insertion torque and the pullout force using bone surrogates of different densities. The indentation force and insertion torque were measured using a custom made test bench during screw insertion into polyurethane foam blocks. The two variables were significantly correlated to pullout force and to density. A high correlation was also found between indentation force and the peak insertion torque. The proposed methods for measuring indentation force and screw insertion torque were reproducible. This study suggests that the peak screw insertion torque and the indentation force can predict the screw fixation strength in synthetic bone models. Additional tests should be performed on animal and human specimens to confirm and to translate these findings to clinical applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.297
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2014
Admission routes2
Has abstractyes

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