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Nasal Floor Elevation Combined with Dental Implant Placement

2010· article· en· W2164492137 on OpenAlexvenueno aff
Ziv Mazor, Adi Lorean, Eitan Mijiritsky, Liran Levin

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2010
Typearticle
Languageen
FieldMedicine
TopicNasal Surgery and Airway Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineImplantDentistryDental implantImplant failureSurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of the present study was to report on the survival of dental implants placed in conjunction with nasal floor elevation. METHODS: A retrospective cohort of 32 consecutive patients from two private practices was evaluated. All patients presented with alveolar bone height deficiency in the anterior region, which was not sufficient to place a dental implant according to a computed tomography (CT) scan preformed prior to implantation. Elevation and augmentation of the nasal mucosa was performed simultaneously with dental implant placement. Data collection included demographic information, as well as records of the pre-operative available bone height, implant dimensions, bone addition following nasal floor augmentation, and survival of the implants at last follow-up. RESULTS: Overall, 32 patients received 100 implants that were performed in conjunction with nasal floor elevation. The average pre-operative available bone height according to a CT scan that was preformed prior to implantation was 9.1 ± 0.9 mm and ranged from 7.3 to 11.2 mm. Bone addition following nasal floor augmentation was 3.4 ± 0.9 mm and ranged between 1.1 and 5.7 mm. The mean follow-up time was 27.8 ± 12.4 months, and during that follow-up period, no implant failure was recorded, resulting in 100% implant survival. CONCLUSION: Nasal floor elevation might serve as a predictable procedure, which allows implant placement in areas with significant atrophy together with increased implant stability due to the bicortical support.

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)0.001
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 score0.160
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.079
GPT teacher head0.431
Teacher spread0.352 · 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

Citations30
Published2010
Admission routes1
Has abstractyes

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Same venueClinical Implant Dentistry and Related ResearchSame topicNasal Surgery and Airway StudiesFrench-language works237,207