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Record W2083340516 · doi:10.1002/jbm.b.30197

Cortical bone screw fixation in ionically modified apatite cements

2005· article· en· W2083340516 on OpenAlexaff
Jake E. Barralet, C. O. Duncan, M.S. Dover, D. C. Bassett, Hiroaki Nishikawa, A. Monaghan, Uwe Gbureck

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2005
Typearticle
Languageen
FieldMedicine
TopicFacial Trauma and Fracture Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsCementMaterials scienceApatiteCortical boneCalcium phosphate cementBone cementFixation (population genetics)PerpendicularComposite materialDental cementDentistryBiomedical engineeringChemistryMineralogyAnatomyMedicine

Abstract

fetched live from OpenAlex

Hydroxyapatite cements are used in reconstruction of the face; usually in well-defined cavities where the cement can be stabilized without the need for internal fixation. A hydroxyapatite cement that could enable screw fixation and some loading therefore has considerable potential in maxillofacial reconstruction. It has been demonstrated recently that water demand of calcium phosphate cements can be reduced by ionically modifying the liquid component. This study investigated the capacity of an ionically modified precompacted apatite cement to retain self-tapping cortical bone screws. Screw pullout forces were determined in the direction of the screw long axis and perpendicular to it, using cortical bone and polymethylmethacrylate cement as a control. In bending pullout tests, measured forces to remove screws from ionically modified precompacted cement were insignificantly different from cortical bone. However, pullout forces of bone screws from hydroxyapatite cement decreased with aging time in vitro.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.079
GPT teacher head0.382
Teacher spread0.303 · 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

Citations12
Published2005
Admission routes1
Has abstractyes

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Same venueJournal of Biomedical Materials Research Part B Applied BiomaterialsSame topicFacial Trauma and Fracture ManagementFrench-language works237,207