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Vertical Bone Augmentation Using Different Osteoconductive Scaffolds Combined with Barrier Domes in the Rat Calvarium

2012· article· en· W1560822446 on OpenAlexvenueno aff
Hadar Zigdon, Dina Lewinson, Tova Bick, Eli E. Machtei

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2012
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsnot available
Fundersnot available
KeywordsDome (geology)DentistryPerforationFixation (population genetics)AnatomyChemistryMedicineMaterials scienceGeologyComposite material

Abstract

fetched live from OpenAlex

PURPOSE: To compare the regenerative potential for vertical bone augmentation of various osteoconductive scaffolds when used in conjunction with barrier domes. MATERIALS AND METHODS: Following exposure and perforation of the calvarium, a gold occlusive dome was filled with the tested scaffold and anchored by fixation screws. Flaps were repositioned and secured. The four treatment groups, three to five rats each, were as follows: Bio-Oss collagen (BOC), β-tricalcium phosphate (TCP), collagen sponge (COL), and empty domes (C). Rats were sacrificed 8 weeks later, and specimens were prepared for histological and histomorphometric analysis. Vertical bone height and total tissue height were measured. RESULTS: The newly regenerated bone appeared mature, highly vascularized, and with no signs of inflammation. Vertical bone height in the TCP group (mean 2.04 ± 0.2 mm) was greater than all other groups (0.76 ± 0.02, 1.52 ± 0.18, and 1.77 ± 0.61 mm for the BOC, C, and COL, respectively) but significantly only for the BOC group (p = .0145). Total tissue height was significantly higher (p < .0001) in both BOC and TCP groups (4.48 ± 0.23 and 5.5 ± 0.24 mm, respectively) compared with COL (3.22 ± 0.11 mm) and C (2.39 ± 0.3 mm) groups. CONCLUSION: TCP in conjunction with barrier dome resulted in greater vertical bone augmentation in the calvarium of rats.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.719
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.085
GPT teacher head0.388
Teacher spread0.302 · 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

Citations24
Published2012
Admission routes1
Has abstractyes

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