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Record W2559896776 · doi:10.1002/adhm.201600947

Surrogate Outcome Measures of In Vitro Osteoclast Resorption of β Tricalcium Phosphate

2016· article· en· W2559896776 on OpenAlexfundno aff
Susan Clarke, Joanne L. Martin, John Nelson, Jean‐Christophe Hornez, Marc Bohner, Nicholas Dunne, Fraser Buchanan

Bibliographic record

VenueAdvanced Healthcare Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsnot available
FundersQueen's UniversityQueen's University BelfastDepartment for Employment and Learning, Northern Ireland
KeywordsOsteoclastResorptionPhosphateIn vitroDentistryMaterials scienceChemistryMedicineInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

Introduction of porosity to calcium phosphate scaffolds for bone repair has created a new challenge when measuring bioresorption in vitro, rendering traditional outcome measures redundant. The aim of this study is to identify a surrogate endpoint for use with 3D scaffolds. Murine RAW 264.7 cells are cultured on dense discs of β-tricalcium phosphate in conditions to stimulate osteoclast (OC) formation. Multinucleated OCs are visible from day 6 with increases at days 8 and 10. Resorption pits are first observed at day 6 with much larger pits visible at days 8, 10, and 12. The concentration of calcium ions in the presence of cells is significantly higher than cell-free cultures at days 3 and 9. Using linear regression analysis, Ca ion release could account for 35.9% of any subsequent change in resorption area. The results suggest that Ca ion release is suitable to measure resorption of a beta-tricalcium phosphate ceramic substrate in vitro. This model could replace the more accepted resorption pit assay in circumstances where quantification of pits is not possible, e.g., when characterizing 3D tissue engineered bone scaffolds.

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)0.001
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.035
GPT teacher head0.280
Teacher spread0.245 · 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

Citations10
Published2016
Admission routes1
Has abstractyes

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