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Record W2573440700 · doi:10.1097/id.0000000000000558

Grafting and Dental Implantation in Patients With Jawbone Cavitation

2017· article· en· W2573440700 on OpenAlexfundno aff
Yawei Chen, Miguel Simancas‐Pallares, Mauro Marincola

Bibliographic record

VenueImplant Dentistry · 2017
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsnot available
FundersFaculty of Medicine and Dentistry, University of Alberta
KeywordsMedicineImplantPremolarDentistryBone graftingMandible (arthropod mouthpart)MolarDental implantSurgery

Abstract

fetched live from OpenAlex

PURPOSE: Jawbone cavitation (BC) is not uncommon and is considered to be related to some cases of unexpected implant displacement into deep jawbone space. Here, a series of cases with BC is described, in which the lesions were accidentally found and successfully treated by bone grafting and dental implantation. METHODS: Thirty-four partially edentulous patients who were found to have BC during dental implant surgeries were included in this study. Alloplast bone substitute (β-tricalcium phosphate) grafting with immediate or staged locking-taper implant placement was performed. Bone filling and implants on BC were followed up to 36 months, and they were evaluated clinically and radiographically to verify treatment outcome. RESULTS: A total of 41 BCs were found at premolar and molar regions, which involved one or more teeth breadth. Nearly most of the lesions occurred in the mandible (95.1%, 39/41). Histologically, they were compatible with focal osteoporotic marrow defects. Fifty-two locking-taper implants and final restorations were delivered on 38 BCs. One implant failed due to loss of integration. The overall cumulative 3-year implant survival rate was 98.1%. CONCLUSION: By carefully examining and managing the surgical bed, the current treatment modality was shown to yield a satisfactory outcome for restoration of edentulous ridge with underneath BC.

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)0.000
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 score0.005
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.299
Teacher spread0.285 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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