The micromechanical behavior of implant‐abutment connections under a dynamic load protocol
Bibliographic record
Abstract
BACKGROUND: The implant-abutment connection (IAC) is known to be a key factor for the long-term stability of peri-implant tissue. PURPOSE: The aim of the present in vitro study was to detect and measure the mechanical behavior of different IACs by X-ray imaging. MATERIALS AND METHODS: A total of 20 different implant systems with various implant dimensions and IACs (13 conical-, 6 flat-, and 1 gable-like IAC) have been tested using a chewing device simulating dynamic and static loading up to 200 N. Micromovements have been recorded with a high-resolution, high-speed X-ray camera, and gap length and gap width between implant and abutment have been calculated. Furthermore, X-ray video sequences have been recorded to investigate the sealing capacity of different IACs. RESULTS: Out of the 20 implant systems, eight implant systems with a conical IAC showed no measurable gaps under static and dynamic loading (200 N). By contrast, all investigated implant systems with a flat IAC showed measurable gaps under dynamic and static loading. X-ray video sequences revealed that a representative conical IAC had sufficient sealing capacity. CONCLUSION: Within the limits of the present in vitro study, X-ray imaging showed reduced formation of microgaps and consecutive micromovements in implants with conical IAC compared to flat IACs.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".