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Record W2549988268 · doi:10.1177/2041731416680319

Bioactive mesoporous wollastonite particles for bone tissue engineering

2016· article· en· W2549988268 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Tissue Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
Fundersnot available
KeywordsWollastoniteScanning electron microscopeMesoporous materialMaterials scienceApatiteBiomedical engineeringDeposition (geology)Bioactive glassMineralogyComposite materialChemistryMedicineGeologyCatalysis

Abstract

fetched live from OpenAlex

The current investigation was aimed at identifying the role of mesoporous wollastonite particles on the healing of rat tibial bone defect. The bone defect was created with a 3-mm-diameter dental drill, and it was filled with mesoporous wollastonite particles. After second and fourth weeks of filling treatments, it was found that mesoporous wollastonite particles promoted bone formation as evidenced by X-ray, histological, scanning electron microscope, and energy-dispersive spectra studies. X-ray study showed the closure of drill hole as seen by high-dense radio-opacity image. Histological analysis depicted the deposition of collagen in the bone defect area in response to mesoporous wollastonite particles' treatment. Scanning electron microscope-energy-dispersive spectra analyses of the sectioned implants also identified the deposition of apatite by these particles. Thus, our results suggested that mesoporous wollastonite particles have bioactive properties, and they can be used as a suitable filling material for promotion of bone formation in vivo.

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.

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 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: none
Teacher disagreement score0.573
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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