Screw stability of <scp>CAD‐CAM</scp> titanium and zirconia abutments on different implants: An in vitro study
Bibliographic record
Abstract
BACKGROUND: Limited information is available regarding the removal torque values (RTVs) of screws of different abutment materials when used with different implants. PURPOSE: To evaluate the effect of implant type and abutment material (zirconia; Zir and titanium; Ti) on the RTVs of abutment screws after cyclic loading. MATERIALS AND METHODS: Internal conical connection implants (CC, OsseoSpeed TX) and modified internal conical connection implants (MCC, OsseoSpeed EV) (n = 10) were clamped in resin dies. Zir and Ti (N = 20) custom abutments were tightened to implants (20 Ncm for CC and 25 Ncm for MCC) as specified by their manufacturers. The abutments were cyclically loaded by using a sequentially increased loading protocol; 2-million cycles under 100 N, 3-million cycles under 200 N, and 2-million cycles under 300 N loads with 2 Hz. After 7 million cycles, RTVs (Ncm) were measured by using a torque gauge. The data were analyzed with ANOVA by using the restricted maximum likelihood estimation method. Tukey-Kramer adjustment was used for any significant interaction of implant-abutment pairs (α = 0.05). RESULTS: Two CC implants, 1 with Ti and 1 with Zir abutment, fractured during cyclic loading (under 300 N loads within the 6th and 7th million cycles) and discarded for statistical analysis. Only implant type had a significant effect on RTVs (p <0.001). Screws of Ti abutments on MCC implants had significantly higher RTVs than the screws of Ti (p = 0.003) and Zir (p = 0.005) abutments on CC implants. CONCLUSIONS: Implant type affected the RTVs, however, the RTVs were higher than the initial torque values for all groups. Screws of Ti abutments on implant, which required greater initial torque values had higher RTVs than the screws of Ti and Zir abutments on the implant that required smaller initial torque values. RTVs were similar for Zir and Ti abutment screws within each implant type.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".