The effect of screw torque maintenance and re-applying screw torque on the retention of prosthetic screw for multi-unit implant restorations
Bibliographic record
Abstract
Purpose: To investigate and compare different multi unit abutment prosthetic screw tightening protocols in terms of reverse torque values (RTV). Additionally, the effect of torquing and retorquing the same screw on RTV is also investigated. Methods: Nine implants (Nobel Replace Select, Nobel Biocare) with straight multiunit abutments and overlying temporary abutments help in place with prosthetic screw were placed in cold-cure acrylic (ProBase Cold, Ivoclar). Nine screw tightening protocols were tested: 1) torque to 15Ncm, 2) hold torque for 10 seconds, 3) hold torque for 20 seconds, 4) retorque at 2 minutes, 5) retorque at 10 minutes, 6) hold torque for 10 seconds and retorque at 2 minutes, 7) hold torque for 10 seconds and retorque at 10 minutes, 8) hold torque for 20 seconds and retorque at 2 minutes, 9) hold torque for 20 seconds and retorque at 10 minutes. Peak RTVs were measured after 30 minutes using a torque meter (MTT03-05, Mark-10). Each prosthetic screw was changed after each insertion with 10 total screws used per multiunit abutment. After determining the protocol with the highest average RTV, the specific protocol was used to evaluate if repeated cycles of insertion and removal on a single screw would have an impact on the reverse torque values. A single screw was torqued with the specified protocol and de-torqued after 30 minutes with the RTV measured. This was conducted another nine times over for a total of 10 cycles. Results: Protocol 5 (retightening after 10 minutes) had the highest average reverse torque value of 12.83 ± 0.16 Ncm and the Protocol 1 (control) had the lowest at 10.64 ± 0.18 Ncm with statistically significant difference. Conclusion: Different Implant screw tightening protocols can influence the reverse torque values. Multiple insertions of an implant screw may increase the reverse torque value.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".