The Staggered Installation of Dental Implants and Its Effect on Bone Stresses
Bibliographic record
Abstract
PURPOSE: The aim of this study was to investigate the effect of offsetting the middle or peripheral implant on the compressive stress values in the crestal bone around the neck of the dental implant. MATERIALS AND METHODS: Three finite element models describing three titanium implants installed in quadrilateral pieces of bone was executed. A 2-mm nickel chromium superstructure representing a bridge was modeled over the implant abutments. In model 1, implants were installed along a straight line. Model 2 had the middle implant installed outside the line connecting the two peripheral implants buccally. Model 3 had the mesial implant installed out of alignment. Six 100-N loads were modeled on top of the mesial and middle implants of the three models individually. Loads 1 and 2 were directed vertically on the mesial and middle implants, while loads 3 and 4 represented the horizontal loads in the buccal direction. Loads 5 and 6 were directed mesially on the mesial and central implants. Maximal compressive stress levels in the crestal bone of the three models were then investigated. RESULTS: The results demonstrated that offset implant installation revealed slightly lower bone stresses under buccally or lingually directed horizontal forces. Slightly higher bone stresses under vertical loads were observed. Horizontal mesial or distal loads resulted in slightly higher bone stresses than those caused by buccal or lingual loading. CONCLUSIONS: The in-line implant alignment clearly had the safest compressive stress outcome on the surrounding structure under vertical loads. Under buccolingual loads, implant alignment with peripheral offset would have, relatively, the safest compressive stress outcome on bone.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".