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Record W2329983261 · doi:10.1097/id.0000000000000194

Effect of Buccal Gap Distance on Alveolar Ridge Alteration After Immediate Implant Placement

2015· article· en· W2329983261 on OpenAlexaff
Warunee Pluemsakunthai, Bach Le, Shohei Kasugai

Bibliographic record

VenueImplant Dentistry · 2015
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsHolland Bloorview Kids Rehabilitation Hospital
FundersCore Research for Evolutional Science and Technology
KeywordsBuccal administrationSoft tissueDentistryPremolarResorptionImplantDental alveolusBone resorptionAlveolar ridgeMedicineAlveolar crestOrthodonticsMolarSurgeryPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: The buccal bone resorption and the deformation of soft tissue contour are major problems of immediate implant treatment. This study aims to examine the changes of alveolar bone and soft tissue after immediate implant placement in different buccal gap distances. MATERIALS AND METHODS: Eight implants were placed randomly in the mandibular premolar sockets of 6 hybrid dogs with 1, 2, and 3 mm buccal gap distances. The dogs were killed after 2 or 4 months for morphometric and microcomputed tomography analyses. DISCUSSION: After 2 months, the 3-mm group had the highest buccal bone volume (BV), buccal bone/soft tissue thickness, and the lowest bone resorption. The wider the buccal gap, the more buccal bone and soft tissue were formed in this experimental setting. After 4 months, the buccal BV had decreased significantly in the 1-mm and the 2-mm groups, whereas the 3-mm group resisted to buccal bone resorption. This difference was more pronounced at the crest. CONCLUSION: The 3 mm is the optimal gap distance among the groups examined, which drastically influences the healing of bone and soft tissue surrounding the implants.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.697
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.023
GPT teacher head0.328
Teacher spread0.305 · 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.

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

Citations30
Published2015
Admission routes1
Has abstractyes

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