Effect of Implant Angulation on Attachment Retention in Mandibular Two‐Implant Overdentures: A Clinical Study
Bibliographic record
Abstract
PURPOSE: Attachment wear can affect the performance of mandibular two-implant overdentures (IODs). This prospective clinical study aimed to investigate the effect of interimplant angulation on the retention achieved by two attachment systems at different time points within 1 year of wearing IODs. MATERIALS AND METHODS: Twenty-four patients (mean age = 73.2 years; standard deviation (SD) = 3.1) wearing IODs opposed by conventional maxillary complete dentures were randomly assigned to two groups in two-by-two crossover design. Retentive Anchor (RA) and Locator (LA) were installed in the IODs of both groups for 1 year, sequentially. Coronal and sagittal interimplant angulation were measured on posterior-anterior and lateral cephalometric radiographs. Retention was measured at baseline, 1 week, 3, 6, and 12 months postattachment installation. Data were analyzed using mixed models with α = 0.05. RESULTS: Mean coronal and sagittal interimplant angulations were 4.6 (SD = 2.9) and 3.5 (SD = 2.6) degrees, respectively. Only with LAs a statistically significant decrease was found in retention (average 1.1 Newton; standard error = 0.38; p = .007) per 1 degree increased sagittal interimplant angulation. CONCLUSIONS: Increased interimplant angulation appears to have higher impact on the retention of LA than of RA attachments. The effect of larger interimplant angulation on the loss of attachment retention and its clinical implications should be further assessed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".