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Effects of Flapless Implant Surgery on Soft Tissue Profiles: A Prospective Clinical Study

2009· article· en· W1578150497 on OpenAlexvenueno aff
Du‐Hyeong Lee, Byung‐Ho Choi, Seung-Mi Jeong, Feng Xuan, Ha-Rang Kim

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2009
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsSoft tissueImplantMedicineDentistryCoronal planeDental implantOsseointegrationHard tissueOrthodonticsSurgeryAnatomy

Abstract

fetched live from OpenAlex

BACKGROUND: Flapless implant surgery has been suggested as a suitable treatment modality for the preservation of soft tissue after implant placement. PURPOSE: The purpose of this study was to determine the extent of soft tissue profile changes around implants after flapless implant surgery. MATERIALS AND METHODS: A total of 44 patients received 76 implants using a flapless implant procedure. The marginal level of the peri-implant soft tissue was evaluated using dental casts 1 week, 1 month, and 4 months after implant placement. RESULTS: The mean soft tissue levels around implants showed 0.7 ± 0.3 mm of coronal growth 1 week after surgery. At 1 month, the levels were 0.2 ± 0.2 mm coronal growth and at 4 months, the values were 0.0 ± 0.3 mm. Soft tissue profiles assessed 4 months after flapless implant placement were similar to profiles assessed immediately before implant placement. CONCLUSION: Flapless implant surgery is advantageous for preserving mucosal form surrounding dental 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.113
GPT teacher head0.496
Teacher spread0.383 · 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 source (direct Gemma or distilled Codex), 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

Citations19
Published2009
Admission routes1
Has abstractyes

Explore more

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