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Record W4388153442 · doi:10.1016/j.sdentj.2023.10.023

Clinical outcomes of dental implants placed in fresh sockets: A five-year retrospective study

2023· article· en· W4388153442 on OpenAlexaff
Roohollah Naseri, Mohammadjavad Shirani, Narges Pouremadi

Bibliographic record

VenueThe Saudi Dental Journal · 2023
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsUniversity of Saskatchewan
FundersDental Research CenterIsfahan University of Medical Sciences
KeywordsMedicineDentistryRadiographyRetrospective cohort studyImplantOrthodonticsSurgery

Abstract

fetched live from OpenAlex

Purpose: This retrospective clinical study aimed to evaluate the implants placed in fresh sockets and investigate the effect of varied oral health conditions and treatment plan details on the clinical and radiographic outcomes. Materials and methods: Fifty-nine participants (102 implants) were included in this study. Four variables, including mean probing depth (PD), mean marginal bone loss (MBL), pink esthetic score (PES), and patient satisfaction, were significant dependent variables, and the effects of independent variables on these four items were studied. The data were analyzed by the analysis of covariance (ANCOVA) using a statistical software. Results: The mean follow-up period was 4.75 ± 1.74 years, and the mean MBL was 1.21 ± 0.81 mm. The survival rate was 97 %. There were significant effects of the finish line site, keratinized gingival width, and attached gingival width on PD after adjusting the factors. Also, the implant brand, plaque index, and uncemented prosthesis affected MBL significantly. In addition, significant effects of the surgeon, implant brand, and proximal contact on PES were found. Conclusion: More PD was found around restorations with a finish line site > 1.5 mm subgingival. Sufficient attached gingiva was a more effective factor on PD than keratinized gingiva. Implants with more plaque scores showed more MBL.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.040
GPT teacher head0.384
Teacher spread0.344 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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