Effects of dental implant diameter and tapered body design on stress distribution in rigid polyurethane foam during insertion
Bibliographic record
Abstract
Anchorage, evaluated by the maximum insertion torque (IT), refers to mechanical engagement between dental implant and host bone at the time of insertion without external loads. Sufficient anchorage has been highly recommended in the clinic. In several studies, the effects of implant diameter and taper body design under external loading have been evaluated after insertion; however, there are few studies, in which their effects on stress distribution during insertion have been investigated to understand establishment of anchorage. Therefore, the objective of this study was to investigate the effects of dental implant diameter and tapered body design on anchorage combining experiments, analytical modelling, and finite element analysis (FEA). Two implant designs (parallel-walled and tapered) with two implant diameters were inserted into rigid polyurethane (PU) foam with corresponding straight drill protocols. The IT was fit to the analytical model (R2 = 0.88-1.0). The insertion process was modelled using explicit FEA. For parallel-walled implants, normalized IT and final FEA contact ratio were not related to the implant diameter while the implant diameter affected normalized IT (R2 = 0.90, p<0.05, β1 = 0.20 and β2 = 0.93, standardized regression coefficients for implant diameter and taper body design) and final FEA contact ratio of tapered implants. The taper design distributed the PU foam stress further away from the thread compared to parallel-walled implants, which demonstrated compression in PU foam established by the tapered body during insertion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".