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Record W2227398395 · doi:10.1002/jbm.a.30696

A novel tool for high‐resolution transmission electron microscopy of intact interfaces between bone and metallic implants

2006· article· en· W2227398395 on OpenAlexaff
Håkan Engqvist, Gianluigi A. Botton, Martin Couillard, S. Mohammadi, Johan Malmström, Lena Emanuelsson, Leif Hermansson, M. W. Phaneuf, Peter Thomsen

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2006
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsFibics (Canada)McMaster UniversityBrockhouse Institute for Materials Research
Fundersnot available
KeywordsMaterials scienceTransmission electron microscopyBiomedical engineeringElectron microscopeMetalNanotechnologyOpticsMetallurgy

Abstract

fetched live from OpenAlex

A key feature in the understanding of the mechanisms of integration versus rejection of implanted materials is a deepened understanding of the elemental and molecular compositions of the interface zone between the surface of the synthetic man-made material and the biological components of tissue. Intact interfaces between metallic implants and tissues have not been able to image and analyse on the ultrastructural level with the common transmission electron microscopy (TEM) sample preparation techniques. By using focused ion beam microscopy for site-specific preparation of TEM samples, intact interfaces between metal implants and calcified tissue were imaged for the first time. The interface's elemental and crystallographic compositions were determined using energy dispersive X-ray mapping and electron diffraction. The developed technique fulfills a long-sought-for demand to correlate the surface properties of implanted metal prostheses with the fine structure and composition of preserved interfaces with tissues.

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)0.000
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.028
GPT teacher head0.327
Teacher spread0.300 · 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

Citations63
Published2006
Admission routes1
Has abstractyes

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Same venueJournal of Biomedical Materials Research Part ASame topicBone Tissue Engineering MaterialsFrench-language works237,207