The Overuse of the Implant Motor: Effect on the Output Torque in Overloading Condition
Bibliographic record
Abstract
BACKGROUND: The overloading of the motor affects its performance. The output torque of the implant motor under overloading condition has not been reported. PURPOSE: The purpose of this study was to determine the reliability and the tendency of the output torque when an implant motor is consecutively used. MATERIALS AND METHODS: Three implant motors were evaluated: SurgicXT/X-SG20L (NSK), INTRAsurg300/CL3-09 (KaVo), and XIP10/CRB26LX (Saeshin). The output torque was measured using an electronic torque gauge fixed with jigs. For the 40 and 50 Ncm torque settings, 300 measurements were taken at 30 rpm. Repeated measures of analysis of variance (ANOVA) and one-way ANOVA were used to compare the torque values within each group and between the groups. RESULTS: As repeating measures, the output torque values decreased gradually compared with the baseline. In within-group analysis, the different torque value from the first measurement appeared earliest in NSK motor, followed in order by Saeshin and KaVo motors. NSK motor showed a different torque decrease between 40 and 50 Ncm settings (p < .05). Intergroup analysis revealed Saeshin motor to have the least deviation from the baseline, followed by KaVo motor. NSK motor had the most inconsistent torque at the 6, 8, 9, and 10 repeat counts (p < .05). CONCLUSION: The actual torque decreases when the surgical motor is continuously used. The NSK motor showed more significant decreases in torque than KaVo and Saeshin motors in overloading condition.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".