Effect of different maintenance time of torque application on detorque values of abutment screws in full‐arch implant‐supported fixed prostheses
Bibliographic record
Abstract
BACKGROUND: The effect of different maintenance time of torque application and screw loosening in full-arch implant-supported prosthesis remains uninvestigated. PURPOSE: To examine the effect of different maintenance time of torque application on detorque values of implant abutment screw in full-arch implant-supported fixed complete denture. MATERIALS AND METHODS: Passively fitting framework supported by four implants stabilized on resin model torqued to 35 N-cm and maintained for different times; instant torque application (protocol A), 10 seconds (protocol B), and 30 seconds (protocol C) were used. Detorque values were recorded during removal of the screws. Comparison between mean torque and detorque values were made using paired sample t-test. The mean removal torque values of each protocol were compared using three-way analysis of variance (ANOVA). RESULTS: The mean removal torque values were lower than the applied torque for all the protocols. The highest mean removal torque value was found in the immediate protocol (A) (24.44 ± 1.7), followed by the 30 seconds protocol (C) (23.37 ± 1.75), and then by the 10 seconds protocol (B) (23.35 ± 1.6). All these differences were found to be statistically significant between torque and detorque values (P = .001). However, the differences among detorque values were not statistically significant (P > .05). CONCLUSION: The application of 35 N-cm for different maintenance time of torque application on implant abutment screw did not appear to affect the detorque value in a multiple implant-supported fixed prosthesis. Maintaining the torque for prolonged time (10 seconds or 30 seconds) was not significantly associated with higher preload than instant torque application in full-arch implant-supported prosthesis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".