Analyzing the Influence of a New Dental Implant Design on Primary Stability
Bibliographic record
Abstract
BACKGROUND: The macrogeometry of dental implants strongly influences the primary stability and hence the osseointegration process. PURPOSE: Compare the performance of conventional and modified implant models in terms of primary stability. MATERIALS AND METHODS: A total of 36 implants (Neodent®) with two different formats (n = 18): Alvim CM (Conical CM, Ø 4.3 mm × 10 mm in length) and Titamax Ti (Cylindrical HE, Ø 4.0 mm × 11 mm in length) were inserted into artificial bone blocks. Nine implants from each set were selected to undergo external geometry changes. The primary stability was quantified by insertion torque and resonance frequency using an Osstell device and the pullout test. One-way analysis of variance and Tukey's test were used for statistical evaluation. RESULTS: The comparative analysis of the implants showed a significant increase of the insertion torque for the modified Conical CM implants (p = 0.000) and Cylindrical HE (p = 0.043); for the resonance frequency the modified Cylindrical HE showed a lower statistical mean (p = 0.002) when compared to the conventional model, and in the pullout test both modified implants showed significant reduction (p = 0.000). CONCLUSIONS: Within the limitations of this study, the proposed modification showed good stability levels and advantages when compared to the conventional implants.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".