Effect of simulated masticatory loading on the retention of stud attachments for implant overdentures
Bibliographic record
Abstract
This study assessed the effect of simulated mastication on the retention of two stud attachment systems for 2-implants overdentures. Sixteen specimens, each simulating an edentulous ridge with implants and an overdenture were divided into two groups, according to the attachment system: Group I (Nobel Biocare ball-socket attachments) and Group II (Locator attachments). Retention forces were measured before and after 400,000 simulated masticatory loads in a customised device. Data were compared by two-way anova followed by Bonferroni test (α = 0·05). Group I presented significantly lower retention forces (Newtons) than Group II at baseline (10·6 ± 3·6 and 66·4 ± 16·0, respectively). However, differences were not significant after 400,000 loads (7·9 ± 4·3 and 21·6 ± 17·0). The number of cycles did not influence the measurements in Group I, whereas a non-linear descending curve was found for Group II. It was concluded that simulated mastication resulted in minor changes for the ball attachment tested. Nevertheless, it reduced the retention of Locator attachments to 40% of the baseline values, what suggests that mastication is a major factor associated with maintenance needs for this system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".