Comparative analysis of stress distribution in implant attachment versus cantilever prostheses
Bibliographic record
Abstract
Objectives: Dental implants are essential for replacing missing teeth, but anatomical constraints can result in implant sites being distant from adjacent teeth. Cantilever implant prosthetics offer an alternative solution by bridging this gap. This study compares the efficacy of cantilever implant prosthetics with conventional implant placement directly adjacent to teeth. Material and Methods: Using cone beam computed tomography, 3-D models were constructed to accurately represent the anatomy of teeth 4, 5, and the surrounding jawbone. Two models were examined: (1) a pontic with a cantilever implant abutment and (2) a pontic connected to both an implant and a tooth abutment. Simulation testing applied forces of 100 Newton in vertical, horizontal, and 45° directions to mimic functional loads on anterior teeth. Analytical processes were conducted using specialized finite element analysis software. Results: In model 1, tension levels under a 100 N force at a 45° angle were 80.1 in the implant and 66.5 in the implant’s crown. In model 2, tensions decreased to 58.26 in the implant and 35.8 in the crown under the same conditions. Connecting the tooth to the implant significantly reduced the load on both components, indicating potential biomechanical benefits. Conclusion: The attachment of dental implants to adjacent teeth presents a suitable alternative for patients with challenging anatomical considerations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".