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Record W2945922506 · doi:10.1111/cid.12791

Morphology and severity of peri‐implantitis bone defects

2019· article· en· W2945922506 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueClinical Implant Dentistry and Related Research · 2019
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsnot available
FundersUniversidad de ExtremaduraNational Center for Theoretical Sciences
KeywordsPeri-implantitisMedicineDentistryImplantLogistic regressionDehiscenceSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Peri-implant defect morphology has shown to potentially impact upon the reconstructive outcomes for the management of peri-implantitis. Given the role that defect morphology plays upon the decision-making in the treatment of peri-implantitis, the present study aimed at assessing the morphology and severity of peri-implantitis bone defects and to insight on the patient-, implant- and site-related variables associated to these. MATERIAL AND METHODS: A cone-beam computed tomography study was carried out to classify peri-implantitis defects according to the type of defect, number of remaining bony walls and severity according to the extension of vertical bone loss. Three major defect categories were proposed: class I-infraosseous; class II-horizontal; class III-combined of class I and II. These were then subclassified into: (a) dehiscence; (b) 2/3-wall; and (c) circumferential-type defect. According to the severity the defects were further subclassified into: A: advanced; M: moderate; and S: slight. In addition, 20 site-, implant-, and patient-related variables were analyzed by generalized estimating equations (GEEs) of multilevel logistic regression models. RESULTS: Based on an a priori power calculation, 332 implants were screened in 47 peri-implantitis patients. Of these, 158 peri-implantitis implants were eligible. The most prevalent defect morphology type was class Ib (55%) followed by class Ia (16.5%), and class IIIb (13.9%). On the contrary, the less frequent defect was class II (1.9%). The most frequent degree of severity was M (50.6%) with S (10.1%) being the least prevalent. Buccal bone loss was significantly greater compared to the other bony walls in class I and class III defects. Age was associated with the type of defect. Age and smoking habit were associated with the morphology of the defects, while smoking habit, type of prosthesis and distance to adjacent implant were associated with the severity of the defects (vertical bone loss). CONCLUSION: Peri-implantitis defects frequently course with an infraosseous component and often with buccal bone loss. Certain patient-, implant-, and site-specific variables are related with defect morphology and severity. However, morphological patterns for peri-implantitis bone defects could not be proven (NCT NCT03777449).

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.086
GPT teacher head0.444
Teacher spread0.358 · 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