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Analysis of New Bone Formation Induced by Periosteal Distraction in a Rat Calvarium Model

2011· article· en· W1949581027 on OpenAlexvenueno aff
Nikola Saulačić, Benoît Schaller, Tateyuki Iizuka, Daniel Buser, Cladius Hug, Dieter D. Bosshardt

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsnot available
FundersAO Foundation
KeywordsPeriosteumBone formationAppositionDistractionCalvariaBone resorptionDistraction osteogenesisLatency stageDentistryBone healingMedicineAnatomyChemistryBiologyEndocrinologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: A controlled, gradual distraction of the periosteum is expected to result in the formation of new bone. PURPOSE: This study was designed to estimate the possibility of new bone formation by periosteal distraction in a rat calvarium model. MATERIAL AND METHODS: Sixteen animals were subjected to a 7-day latency period and distraction rate at 0.4 mm/24 hours for 10 days. Two experimental groups with seven rats each were killed at 10 and 20 days of consolidation period and analyzed by means of microcomputed tomography, histologically and histomorphometry. RESULTS: In the central regions underneath the disk device, signs of both bone apposition and bone resorption were observed. Peripheral to the disc, new bone was consistently observed. This new bone was up to two and three times thicker than the original bone after a 10- and 20-day consolidation period, respectively. Signs of ongoing woven bone formation indicated that the stimulus for new bone formation was still present. There were no statistically significant differences regarding bone density, bone volume, and total bone height between the two groups. CONCLUSION: The periosteal distraction model in the rat calvarium can stimulate the formation of considerable amounts of new bone.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.130
GPT teacher head0.430
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations21
Published2011
Admission routes1
Has abstractyes

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