Using an Attachment System with PEEK Matrices for Single-Implant Overdentures: In Vitro Retention Force
Bibliographic record
Abstract
Single-implant overdentures (SIOs) represent a major biomechanical challenge in terms of prosthetic retention. The Novaloc attachment system has the potential to overcome those challenges when used for SIOs, due to the use of PEEK matrices. This study compared the retentive force of the Novaloc attachment to the traditional Locator system, before and after cyclic insertion-removal cycles. Three Novaloc matrices (white, yellow, and green, corresponding to low, medium, and high retention, respectively) and Locator (medium retention) were tested, totalling four groups. Retentive force was measured using an Imada force gauge before and after 1095 insertion-removal cycles, corresponding to a year of SIO wearing. Retention was tested with the implants angulated at 0, 10, and 20°. Data for the different groups, angles, and cycling periods were tested via linear regression analysis and two-way ANOVA (α = 0.05). Although the Locator system yielded higher retention forces in general, it lost a much higher percentage of retention with cycling. This trend was similar with the three angles, with forces being inversely proportional to the implant angulation. The authors conclude that Novaloc may provide more reliable retention for SIOs due to their higher resistance to insertion-removal cycling.
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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 | 0.001 |
| 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".