Testing the retention of attachments for implant overdentures – validation of an original force measurement system
Bibliographic record
Abstract
The aim of this study was to validate an original portable device to measure attachment retention of implant overdentures both in the lab and in clinical settings. The device was built with a digital force measurement gauge (Imada) secured to a vertical wheel stand associated with a customized support to hold and position the denture in adjustable angulations. Sixteen matrix and patrix cylindrical stud attachments (Locator) were randomly assigned as in vitro test specimens. Attachment abutments were secured in an implant analogue hung to the digital force gauge or to the load cell of a traction machine used as the gold standard (Instron Universal Testing Machine). Matrices were secured in a denture duplicate attached to the customized support, permitting reproducibility of their position on both pulling devices. Attachment retention in the axial direction was evaluated by measuring maximum dislodging force or peak load during five consecutive linear dislodgments of each attachment on both devices. After a wear simulation, retention was measured again at several time periods. The peak load measurements with the customized Imada device were similar to those obtained with the gold standard Instron machine. These findings suggest that the proposed portable device can provide accurate information on the retentive properties of attachment systems for removable dental prostheses.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".