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Three-dimensional analytical techniques for evaluation of osseointegrated titanium implants

2014· article· en· W2068910812 on OpenAlexafffund
Anna Thorfve, Anders Palmquist, Kathryn Grandfield

Bibliographic record

VenueMaterials Science and Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaVetenskapsrådetMcMaster University
KeywordsOsseointegrationMaterials scienceMicroscale chemistryBiocompatibilityTitaniumImplantBiomedical engineeringNanoscopic scaleNanotechnologyCharacterization (materials science)CoatingMetallurgyMathematics

Abstract

fetched live from OpenAlex

Osseointegration, the direct bonding of titanium implant materials with bone, is critical for implant success where nanostructured surface features contribute to nano-osseointegration. However, we also know that features and processes on the microscale influence the biocompatibility of implant materials. We highlight the advantages of using mutlilength scale analyses, focusing on three-dimensional techniques, ranging from X-ray microcomputed tomography, to focused ion beam, to high resolution electron tomography to identify markers of osseointegration. A titanium implant with modified biomimetic coating studied in vitro and in vivo at various time points is used to exemplify the complementary information gained from three-dimensional analyses from the micro- to nanoscale.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.264
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 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
GenreMethods

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

Citations14
Published2014
Admission routes2
Has abstractyes

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