The Influence of Insertion Torque on Stress Distribution in Peri-Implant Bones Around Ultra-Short Implants: An FEA Study
Bibliographic record
Abstract
Using ultra-short dental implants is a promising alternative to extensive bone grafting procedures for patients with atrophic posterior mandibles and vertical bone loss. However, the amount of insertion torque (IT) applied during implant placement significantly influences stress distribution in the peri-implant bone, which affects implant stability and long-term success. MATERIALS AND METHODS: This study used finite element analysis (FEA) to examine how different insertion torques (35 N·cm and 75 N·cm) affect stress distribution in cortical and trabecular bone types D2 and D4 surrounding ultra-short implants. Von Mises equivalent stress values were compared with ultimate bone strength thresholds to evaluate the potential for microdamage during insertion. RESULTS: The findings demonstrate that increasing IT from 35 N·cm to 75 N·cm led to a significant increase in peri-implant bone stress. Specifically, cortical bone stress in D4 bone increased from approximately 79 MPa to 142 MPa with higher IT, exceeding physiological limits and elevating the risk of microfractures and bone necrosis. In contrast, lower IT values kept stress within safe limits, ensuring optimal primary stability without damaging the bone. These results underscore the need to strike a balance between achieving sufficient implant stability and avoiding mechanical trauma to the surrounding bone. CONCLUSIONS: Accurate control of insertion torque during the placement of ultra-short dental implants is crucial to minimize bone damage and promote optimal osseointegration. Excessive torque, especially in low-density bone, can compromise implant success by inducing excessive stress, thereby increasing the risk of early failure.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".