Immediate Loading of Two Implants with a Mandibular Implant-Retained Overdenture: A New Treatment Protocol
Bibliographic record
Abstract
PURPOSE: The aim of this study was to present the clinical outcomes of the immediate loading of two bar-splinted implants retaining a mandibular overdenture. MATERIALS AND METHODS: In a clinical trial, 124 edentulous patients were treated according to a new treatment concept, which involves the immediate loading of two bar-splinted SLActive implants with an implant-retained mandibular overdenture. The new conventional mandibular denture is used as a template for implant positioning and as an impression tray, and for mounting the retention clip by the dental laboratory. At the same day the implants are placed, the conventional denture is converted into an implant-retained overdenture. During the healing and evaluation period, resonance frequency analysis (RFA) was undertaken to assess the effect of loading on implant stability and survival. RESULTS: The survival rate of the implants was 98.8% during the evaluation period (12-40 months). Only 3 of the 248 implants were lost. During the healing (osseointegration) phase, the implant-stability quotient increased significantly (p = .0001). During the evaluation period, four patients (3%) needed a relining of their mandibular overdenture, whereas 13 patients (11%) needed relining of the maxillary denture. CONCLUSIONS: Two interconnected implants can be successfully loaded by a mandibular overdenture at the same day of implant placement with a high survival rate of the implants. Only a few patients needed additional relining of the overdenture. Repeated RFA measurements can be useful in gauging implant stability and survival.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".