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Record W2897831238 · doi:10.1111/clr.13342

Group 1 ITI Consensus Report: The influence of implant length and design and medications on clinical and patient‐reported outcomes

2018· article· en· W2897831238 on OpenAlexaff
Ronald E. Jung, Bilal Al‐Nawas, Maurício G. Araújo, Gustavo Ávila‐Ortiz, Stephen Barter, Nadine Brodala, Vivianne Chappuis, Bo Chen, André De Souza, Ricardo Faría Almeida, Stefan Fickl, Gary Finelle, Jeffrey Ganeles, Hadi Gholami, Christoph H. F. Hämmerle, Simon Storgård Jensen, Asbjørn Jokstad, Hideaki Katsuyama, Johannes Kleinheinz, Chatchai Kunavisarut, Nikos Mardas, Alberto Monje, Panos Papaspyridakos, Michael Payer, Eik Schiegnitz, Ralf Smeets, Martina Stefanini, Christiaan ten Bruggenkate, Konstantinos Vazouras, Hans‐Peter Weber, Dieter Weingart, Péter Windisch

Bibliographic record

VenueClinical Oral Implants Research · 2018
Typearticle
Languageen
FieldDentistry
TopicDental Implant Techniques and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsImplantMedicineDentistryMedical physicsSurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of Working Group 1 was to address the influence of different local (implant length, diameter, and design) and systemic (medications) factors on clinical, radiographic, and patient-reported outcomes in implant dentistry. Focused questions on (a) short posterior dental implants (≤6 mm), (b) narrow diameter implants, (c) implant design (tapered compared to a non-tapered implant design), and (d) medication-related dental implant failures were addressed. MATERIALS AND METHODS: Four systematic reviews were prepared in advance of the Consensus Conference and were discussed among the participants of Group 1. Consensus statements, clinical recommendations, and recommendations for future research were based on structured group discussions until consensus was reached among the entire expert Group 1. The statements were then presented and accepted following further discussion and modifications as required by the plenary. RESULTS: Short implants (≤6 mm) revealed a survival rate ranging from 86.7% to 100%, whereas standard implant survival rate ranged from 95% to 100% with a follow-up from 1 to 5 years. Short implants demonstrated a higher variability and a higher Risk Ratio [RR: 1.24 (95% CI: 0.63, 2.44, p = 0.54)] for failure compared to standard implants. Narrow diameter implants (NDI) have been classified into three categories: Category 1: Implants with a diameter of <2.5 mm ("Mini-implants"); Category 2: Implants with a diameter of 2.5 mm to <3.3 mm; Category 3: Implants with a diameter of 3.3 mm to 3.5 mm. Mean survival rates were 94.7 ± 5%, 97.3 ± 5% and 97.7 ± 2.3% for category 1, 2 and 3. Tapered versus non-tapered implants demonstrated only insignificant differences regarding clinical, radiographic, and patient-reported outcomes. The intake of certain selective serotonin reuptake inhibitors and proton pump inhibitors is associated with a statistically significant increased implant failure rate. The intake of bisphosphonates related to the treatment of osteoporosis was not associated with an increased implant failure rate. CONCLUSIONS: It is concluded that short implants (≤6 mm) are a valid option in situations of reduced bone height to avoid possible morbidity associated with augmentation procedures; however, they reveal a higher variability and lower predictability in survival rates. Narrow diameter implants with diameters of 2.5 mm and more demonstrated no difference in implant survival rates compared to standard diameter implants. In contrast, it is concluded that narrow diameter implants with diameters of less than 2.5 mm exhibited lower survival rates compared to standard diameter implants. It is further concluded that there are no differences between tapered versus non-tapered dental implants. Certain medications such as selective serotonin reuptake inhibitors and proton pump inhibitors showed an association with a higher implant failure rate.

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)0.007
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.321
GPT teacher head0.543
Teacher spread0.222 · 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.

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

Citations209
Published2018
Admission routes1
Has abstractyes

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