Evaluation of primary stability in modified implants: Analysis by resonance frequency and insertion torque
Bibliographic record
Abstract
Abstract Background Changes in the macrogeometry of dental implants are known to influence primary stability and the osseointegration process. Purpose The purpose of the present in vitro study was to evaluate the mechanical behavior of geometric changes in the apex region of dental implants. Methods Thirty‐five cylindrical dental implants (Titamax Ti; Neodent) were machined at the apical third to reproduce the experimental groups: without apical cut (Wc), apical bi‐split cut, apical tri‐split cut, apical quadri‐split cut (Qs). One (control group) (Titamax Ti Ex) (n = 7) without any modifications was added. The implants had the same final dimensions (4.1 x 11 mm2). All implants were inserted into artificial bone blocks and were evaluated by insertion torque and resonance frequency by ISQ values (Osstell). Two‐tailed analysis of variance (One‐way ANOVA) and Tukey's post‐test (P < .05). Results Control and Qs implants showed a significant increase of the insertion torque (P < .001). For the resonance frequency, Wc and (control) implants had the greatest ISQ values. However, there's no significant difference between (control) and Qs for the ISQ values (P < .001). Conclusion Within the limitations of the present study, the proposed geometries at the apical third of dental implants greatly influenced its insertion torque and primary stability in vitro.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".