SynthesisInternational journal of implant dentistry2025
Prediction models for the complication incidence and survival rate of dental implants-a systematic review and critical appraisal.
Synthesis in International journal of implant dentistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Diagnostic Accuracy of Panoramic Radiography for Assessing Maxillary Posterior Root Protrusion into the Maxillary Sinus: A Cluster-Adjusted Prediction Model Using CBCT as Reference.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence for Radiographic Diagnosis of Peri-Implantitis: A Comprehensive Review on Detection, Measurement, and Risk Stratification.Journal of clinical medicine · 2026Review
- Artificial Intelligence in Implant Dentistry: Clinical Validity, Diagnostic Performance, Surgical Planning, and Medico-Legal Implications-A Narrative Review.Dentistry journal · 2026Review
- Multivariate Analysis of the Survival Rates and Risk Factors of One-Piece Zirconia Implants Supporting Single Crowns or Fixed Dental Prostheses: A Retrospective Cohort Study with Follow-Up Periods of up to 8 Years.Dentistry journal · 2026Article
- The National Dental Practice-Based Research Network Dental Implant Restoration Registry to Evaluate Dental Implant Outcomes in Community Practice Settings: Protocol for a Prospective Observational Study.JMIR research protocols · 2026Observational
- Comparative accuracy analysis of robotic and static guided implant surgery: a retrospective clinical study.BMC oral health · 2026Article
- AI-Powered Predictive Models in Implant Dentistry: Planning, Risk Assessment, and Outcomes.Journal of clinical medicine · 2025Review
- Comparative analysis of the microbiota in gingival crevicular fluid from peri-implantitis and periodontitis using 16 S ribosomal RNA gene amplicon sequencing: a cross-sectional study.BMC oral health · 2025Article
- AI-assisted preoperative surgical planning for dental implant.Journal of translational medicine · 2025Article
- "Could She/He Walk Out of the Hospital?": Implementing AI Models for Recovery Prediction and Doctor-Patient Communication in Major Trauma.Diagnostics (Basel, Switzerland) · 2025Article
- Exploring the Role of Artificial Intelligence in Dental Implantology: A Scholarly Review.Journal of pharmacy & bioallied sciences · 2025Review
- Enhanced peri-implantitis management through purple-LED irradiation coupled with silver ion application and calcium phosphate gene transfection carrier coating.Scientific reports · 2025Article
- A short review on the role of Artificial Intelligence (AI) in dental implants.Bioinformation · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
purposeThis systematic review aims to assess the performance, methodological quality and reporting transparency in prediction models for the dental implant's complications and survival rates.
methodsA literature search was conducted in PubMed, Web of Science, and Embase databases. Peer-reviewed studies that developed prediction models for dental implant's complications and survival rate were included. Two reviewers independently evaluated the risk of bias and reporting quality using the PROBAST and TRIPOD guidelines. The performance of the models were also compared in this study. The review followed the PRISMA guidelines and was registered with PROSPERO (CRD42019122274).
resultsThe initial screening yielded 1769 publications, from which 14 studies featuring 43 models were selected. Four of the 14 studies predicted peri-implantitis as the most common outcome. Three studies predicted the marginal bone loss, two predicted suppuration of peri-implant tissue. The remaining five models predicted the implant loss, osseointergration or other complication. Common predictors included implant position, length, patient age, and a history of periodontitis. Sixteen models showed good to excellent discrimination (AUROC >0.8), but only three had undergone external validation. A significant number of models lacked model presentation. Most studies had a high or unclear risk of bias, primarily due to methodological limitation. The included studies conformed to 18-27 TRIPOD checklist items.
conclusionsThe current prediction models for dental implant complications and survival rate have limited methodological quality and external validity. There is a need for enhanced reliability, generalizability, and clinical applicability in future models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.