In Vitro Assessment of Primary Stability of Straumann® Implant Designs
Bibliographic record
Abstract
BACKGROUND: Primary implant stability (PS) is one of the main factors influencing implant survival rate. Several methods to determine the PS have been used, such as Periotest values (PVs) and resonance frequency analysis (RFA) with implant stability quotient (ISQ) values. PURPOSE: The aim of this study was to compare different implant designs in regard to PS assessed by Periotest and RFA in vitro. MATERIALS AND METHODS: A total of 90 implants were placed in freshly slaughtered cow ribs. The implants (Straumann®, Institute Straumann AG, Basel, Switzerland; length 10 mm, ø3.3 mm) had the following three designs: Bone Level (BL, 30 implants), Standard Plus (SP, 30 implants), and Tapered Effect (TE, 30 implants). Before implant placement, the investigator was calibrated for every design according to the manufacturer's instructions. An independent observer, blinded to the study, assessed the accuracy of placement. RFA based on the Osstell device and PVs were performed after abutment connection. One-way analysis of variance and Tukey's post hoc test were used for statistical evaluation. RESULTS: All implants were mechanically stable. The mean PV for BL was -4.67(± 1.18), for SP, -6.07(± 0.94), and for TE, -6.57(± 0.57). The mean ISQ values were 75.02(± 3.65), 75.98(± 3.00), and 79.83(± 1.85), respectively. The one-way ANOVA showed significant difference among three implant designs in PV (p < .0001) and for the ISQ between BL/TE or SP/TE implants (p < .0001). In addition, the Tukey's (pair-wise comparison) test showed significant differences in PV and RFA between the BL/T (p < .0001). CONCLUSION: Within the limitations of this study, higher implant stability was found for tapered designed 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".