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Record W2039376950 · doi:10.1142/s1793984411000414

EFFECT OF ALENDRONATE-CONTAINING COATINGS ON OSTEOINTEGRATION INTO POROUS TANTALUM IN A CORTICAL BONE MODEL

2011· article· en· W2039376950 on OpenAlexaff
Ke Duan, Youxin Hu, Karen Long, Andrew Toms, Helen M. Burt, Thomas R. Oxland, Bas Masri, Clivе P. Duncan, Donald S. Garbuz, Rizhi Wang

Bibliographic record

VenueNano LIFE · 2011
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOsseointegrationCoatingOsteolysisImplantMaterials scienceCortical boneFixation (population genetics)Biomedical engineeringTantalumCalciumDentistrySurgeryChemistryMedicineComposite materialMetallurgyAnatomy

Abstract

fetched live from OpenAlex

Hip replacement is extensively performed in hips with serious damages. The clinical outcomes of hip implants remain to be improved. Local delivery of bisphosphonates may improve implant fixation by positively affecting local bone modeling. In this study, two alendronate-containing coatings were prepared on porous tantalum by electrolytic deposition. Calcium phosphate coating was deposited and adsorbed with alendronate; the resulting coating had a low drug dose and slow release rate. Solid calcium alendronate coating was also deposited on tantalum; the resulting coating had high drug dose and faster release rate. The effects of the two coatings on new bone formation and implant fixation were studied in the rabbit tibial cortex. Four weeks after implantation, the implants with adsorbed alendronate showed the highest total new bone formation and mechanical fixation, whereas the implants with solid drug coating showed slightly lower fixation and total new bone formation than control bare implants. The improvement by the alendronate-adsorbed calcium phosphate coating provides potentials of enhancing early fixation of porous implants. The solid drug coating warranted further studies to exploit its high drug dose for inhibiting future osteolysis.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.272
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.019
GPT teacher head0.268
Teacher spread0.249 · 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
Published2011
Admission routes1
Has abstractyes

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