Resistance to Wear of Four Matrices with Ball Attachments for Implant Overdentures: A Fatigue Study
Bibliographic record
Abstract
PURPOSE: The study evaluated in vitro the retention force and the wear resistance over simulated function of four matrix components of ball attachments for implant-retained overdentures. MATERIALS AND METHODS: Four types of matrices for ball attachments were evaluated in a fatigue study simulating 5500 cycles of insertion and removal. The matrices used were (1) a Teflon matrix supported by a metal housing, (2) a titanium matrix, (3) a gold alloy matrix, (4) an O-ring matrix using the red color ring for medium retention. Dimensional changes of the ball attachments were investigated with a profilometer. RESULTS: The Teflon matrices showed an increase of 27% in retention at 5500 cycles while the gold alloy matrices showed an increase of 50% in retention in the first 500 cycles and remained relatively stable up to 5500 cycles. On the other hand, titanium matrices and O-ring matrices exhibited progressive loss of retention ending with 68% and 75% of retention loss, respectively, at 5500 cycles. Dimensional analysis by profilometer revealed significant wear on the ball attachment only for titanium matrixes. CONCLUSIONS: Gold alloy and Teflon matrices showed the highest retention values without retention loss after 3 years of simulated function. Titanium and O-ring matrices presented a continuous loss of retention with the highest wear on the ball attachments when combined with the titanium matrix.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".