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Record W2740432173 · doi:10.1002/jbm.b.33962

Multimodal analysis of <i>in vivo</i> resorbable CaP bone substitutes by combining histology, SEM, and microcomputed tomography data

2017· article· en· W2740432173 on OpenAlexaff
Ahmed Sweedy, Marc Bohner, Gamal Baroud

Bibliographic record

VenueJournal of Biomedical Materials Research Part B Applied Biomaterials · 2017
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCeramicResorptionMaterials scienceHistologyBiomedical engineeringBone formationIn vivoX-ray microtomographyDentistryComposite materialPathologyBiologyMedicineRadiology

Abstract

fetched live from OpenAlex

This study introduced and demonstrated a new method to investigate the repair process of bone defects using micro- and macroporous beta-tricalcium phosphate (β-TCP) substitutes. Specifically, the new method combined and aligned histology, SEM, and preimplantation microcomputed tomography (mCT) data to accurately characterize tissue phases found in biopsies, and thus better understand the bone repair process. The results included (a) the exact fraction of ceramic remnants (CR); (b) the fraction of ceramic resorbed and substituted by bone (CSB); and (c) the fraction of ceramic resorbed and not substituted by bone (CNSB). The new method allowed in particular the detection and quantification of mineralized tissues within the 1-10 µm micropores of the ceramic ("micro-bone"). The utility of the new method was demonstrated by applying it on biopsies of two β-tricalcium phosphate bone substitute groups with two differing macropore sizes implanted in an ovine model for 6 weeks. The total bone deposition and ceramic resorption of the two substitute groups, having macropore sizes of 510 and 1220 μm, were 25.1 ± 8.1% and 67.5 ± 3.2%, and 24.4 ± 4.1% and 61.4 ± 6.5% for the group having the larger pore size. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater, 106B: 1567-1577, 2018.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.006
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.038
GPT teacher head0.313
Teacher spread0.275 · 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.

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
Published2017
Admission routes1
Has abstractyes

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