Prevention of peri‐implant disease in edentulous patients with fixed implant rehabilitations
Bibliographic record
Abstract
OBJECTIVES: To provide an overview about the current approaches to prevent peri-implant diseases in edentulous patients with complete-arch implant-supported prostheses, and to review the clinical applications of the latest digital technologies for implant prosthodontics. METHODS: A review of the guidelines to prevent peri-implant diseases in patient's receiving complete-arch implant-supported prostheses including facially driven treatment planning procedures using either conventional or digital methods, computer-aided implant planning procedures, and prosthodontic design variables including the optimal number and distribution of dental implants, implant to abutment connection type, implant or abutment level design, screw- or cement-retained alternatives, prostheses contours, and material selection is provided. Furthermore, an outline of the current therapeutic management approaches to address peri-implant diseases is reviewed. CONCLUSIONS: Clinicians should understand and know different planning and design-related variables that can affect biological and mechanical complication rates of complete-arch implant-supported prostheses. Maintenance protocols are fundamental for minimizing biological and mechanical complications.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".