Maxillary Overdentures Supported by Anteriorly or Posteriorly Placed Implants Opposed by a Natural Dentition in the Mandible: A 1‐Year Prospective Case Series Study
Bibliographic record
Abstract
BACKGROUND: For maxillary overdenture therapy, treatment guidelines are missing. There is a need for longitudinal studies. PURPOSE: The purpose of this 1-year prospective case series study was to assess the treatment outcome of maxillary overdentures supported by six dental implants opposed by natural antagonistic teeth in the mandible. MATERIALS AND METHODS: Fifty patients were treated with a maxillary overdenture supported by six dental implants, either placed in the anterior region (n = 25 patients) or in the posterior region (n = 25 patients). Items of evaluation were the following: survival of implants, condition of hard and soft peri-implant tissues, and patients' satisfaction. RESULTS: One-year implant survival rate was 98% in the anterior group and 99.3% in the posterior group. Mean radiographic bone loss in the anterior and posterior groups after 1 year of loading was 0.22 and 0.50 mm, respectively. Mean scores for plaque, calculus, gingiva, bleeding, and pocket probing depth were low, and patients' satisfaction was high, with no differences between the groups. CONCLUSION: Six dental implants placed in either the anterior region or the posterior region of the edentulous maxilla, connected with a bar, and opposed by antagonistic teeth in the mandible supply a proper base for the support of an overdenture.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".