Number of implants for mandibular implant overdentures: a systematic review
Bibliographic record
Abstract
PURPOSE: The aim of this systematic review is to address treatment outcomes of Mandibular implant overdentures relative to implant survival rate, maintenance and complications, and patient satisfaction. MATERIALS AND METHODS: A systematic literature search was conducted by a PubMed search strategy and hand-searching of relevant journals from included studies. Randomized Clinical Trials (RCT) and comparative clinical trial studies on mandibular implant overdentures until August, 2010 were selected. Eleven studies from 1098 studies were finally selected and data were analyzed relative to number of implants. RESULTS: Six studies presented the data of the implant survival rate which ranged from 95% to 100% for 2 and 4 implant group and from 81.8% to 96.1% for 1 and 2 implant group. One study, which statistically compared implant survival rate showed no significant differences relative to the number of implants. The most common type of prosthetic maintenance and complications were replacement or reattaching of loose clips for 2 and 4 implant group, and denture repair due to the fracture around an implant for 1 and 2 implant groups. Most studies showed no significant differences in the rate of prosthetic maintenance and complication, and patient satisfaction regardless the number of implants. CONCLUSION: The implant survival rate of mandibular overdentures is high regardless of the number of implants. Denture maintenance is likely not inflenced substantially by the number of implants and patient satisfaction is typically high again regardless os the number of implants.
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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".