Long-Term Comprehensive Results of Four-Implant-Supported Overdentures and Fixed Complete Dentures: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: To integrate the medium-term outcomes of four-implant-supported overdentures (IODs) and full-arch fixed restorations (IFRs) in the maxilla. MATERIALS AND METHODS: The search was performed in PubMed, Embase, and Cochrane databases, complemented by manual search. The inclusion criteria were at least 10 maxillary edentulous patients restored by IOD or IFR with at least 5 years of follow-up. Risk of bias (RoB) 2 and Newcastle-Ottawa Scale (NOS) tools were used to assess RoB. The implant survival rate (ISR) was calculated as the primary outcome. Prothesis survival rate, marginal bone loss (MBL), and complications were the secondary outcomes. RESULTS: A total of 16 studies with 5,568 implants met the criteria (9 implants on IODs, 7 implants on IFRs). The weighted ISR of IODs was 94.5% (95% CI [92.1%, 96.9%]; I2 = 84.22%), and subgroup analysis was performed on the attachment system and study type. The weighted ISR of IFRs was 98.5% (95% CI [97.4%, 99.5%]; I2 = 77.88%). For prosthesis survival, the rate of 85.0% in IODs was lower than that of 99.9% in IFRs. MBL after 5 years was -0.27 ± 1.31 mm in IODs and -1.20 ± 0.76 mm in IFRs. Retention loss (0.34 per patient) and dislodgment/fracture of the acrylic teeth (0.09 per patient) were the most common complications in IODs and IFRs, respectively. CONCLUSIONS: Despite the variance of baseline, IFRs had a relatively higher implant and prothesis survival rate than IODs, whereas IODs had less MBL at the 5th year and a higher incidence of complications. Both maxillary IODs and IFRs have predictable medium-term clinical results.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".