Development of a New Implant Primary Stability Parameter: Insertion Torque Revisited
Bibliographic record
Abstract
PURPOSE: The aims of the study are to introduce a new parameter to measure primary stability and to evaluate the possible correlations between this parameter and bone density, initial bone-to-implant contact (IBIC), Resonance Frequency Analysis (RFA), and peak insertion torque (IT). MATERIAL AND METHODS: The study was performed on three different types of fresh humid bovine bone: type I, type II, and type III. A total of 90 XiVE implants (30 per bone type) were used; implant insertion was performed with a calibrated maximum torque of 70 Ncm at predetermined 30 rpm. The IT data were recorded and exported as a curve; using a trapezoidal integration technique, the area underlying the curve was calculated: this area represents the variable torque work (VTW). Furthermore, peak IT and RFA were recorded; finally IBIC was calculated from histological specimens. RESULTS: Spearman correlation analysis of the entire sample reveals that VTW presents a significant (p < .01) positive correlation with bone density; a significant (p < .05) positive correlation with IBIC, and a significant (p < .01) positive correlation with all the other primary stability parameters. Spearman correlation analysis of the three different groups show that VTW presents a significant positive correlation with IT in all three types of bone; on the other hand, VTW shows a negative not significant correlation with RFA in bone I, a positive significant correlation in bone II, and a positive not significant correlation in bone III. Furthermore, VTW shows a negative significant correlation with IBIC in bone I and a positive significant correlation in bone II and III. CONCLUSIONS: Within the limitations of an in vitro study, the VTW seems to be a promising parameter to measure implant primary stability.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".