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Record W2145864934 · doi:10.1111/cid.12250

The Effect of Decortication for Periosteal Expansion Osteogenesis Using Shape Memory Alloy Mesh Device

2014· article· en· W2145864934 on OpenAlexvenueno aff
Kensuke Yamauchi, Shinnosuke Nogami, K. Tanaka, S. Yokota, Yoshinaka Shimizu, Hiroyasu Kanetaka, Tetsu Takahashi

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2014
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsDecorticationPeriosteumBone healingMedicineBone graftingDentistrySurgeryBiomedical engineering

Abstract

fetched live from OpenAlex

BACKGROUND: In conventional bone grafting technique, decortication procedure enhances the healing process and bone regeneration to reach the grafted site more readily. PURPOSE: This study evaluates to improve periosteal expansion osteogenesis (PEO) using a shape memory alloy mesh (SMA) device with decortication in a rabbit model. MATERIALS AND METHODS: The SMA device was inserted under the periosteum at the forehead and pushed, bent, and attached to the bone surface and fixed with a titanium screw. Twelve rabbits were divided into two groups: PEO without decortication (P group) and with decortication (D group). After 2 weeks, the screw was removed, and the mesh was activated by its own elasticity. Rabbits were sacrificed 5 (P1/D1) and 8 (P2/D2) weeks after operation and histologically and radiographically evaluated. RESULTS: The mean activation height was 2.9 ± 0.5 mm. The ratio of new bone volume in the elevated volume was 17.6% in P1, 59.8% in D1 33.4% in P2, and 65.1% in D2. D group had a statistically higher volume of new bone than P group during each period (p < .05). CONCLUSION: PEO with decortication appears to be a promising clinical alternative for bone augmentation and introduces the new concept of "dynamic graft and guided bone regeneration (GBR)."

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)0.004
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.063
GPT teacher head0.398
Teacher spread0.335 · 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

Citations20
Published2014
Admission routes1
Has abstractyes

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