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Record W4391248355 · doi:10.26776/ijemm.09.01.2024.01

An Overview of Bone Cement Compositions used in Vertebroplasty and Their Viability in Clinical Settings

2024· article· en· W4391248355 on OpenAlexaff
Gleb Levashov, Muhammad Hasibul Hasan, Arion Frakulli, Janneza Macaalay, Kian Nahad

Bibliographic record

VenueInternational Journal of Engineering Materials and Manufacture · 2024
Typearticle
Languageen
FieldMedicine
TopicSpinal Fractures and Fixation Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBone cementCementMedicineDentistryMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Vertebroplasty is a minimally invasive surgical procedure wherein a particular composition of bone cement is injected into a fractured vertebra in an attempt to restore joint mobility and reduce perceived pain. It is especially common in the treatment of osteoporotic vertebral compression fractures, most typically experienced by older women. The formulation of this bone cement takes on many forms, the most common being the group of cements known as polymethyl methacrylate acrylic bone cements. The different varieties of acrylic bone cements are investigated and compared, in addition to new potential rival materials being developed to rival the dominance of acrylic bone cements in the vertebroplasty bone cement industry. Factors such as biomechanical strength, handling, osteoconductivity/inductivity, biodegradability, additive delivery, and porosity are considered. While the main drawbacks of acrylic bone cements (significant biomechanical mismatch with vertebrae and lack of biodegradability and osteoconductivity) present opportunities for new solutions to enter the market to compete, the industry standard in vertebroplasty remains the most widely applicable, and thus wisest, cement choice for the procedure. Keywords: Vertebroplasty, Minimally Invasive Surgery, Biodegradability, Bone Cement

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 score0.838
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.350
Teacher spread0.326 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Engineering Materials and ManufactureSame topicSpinal Fractures and Fixation TechniquesFrench-language works237,207