The Effect of Repeated Torque in Small Diameter Implants with Machined and Premachined Abutments
Bibliographic record
Abstract
BACKGROUND: Detorquing value is an important factor in the amount of preload stresses during abutment screw fastening. This study evaluated the percentage of detorque values in two-piece machined titanium and premachined cast abutments in small diameter implants. MATERIALS AND METHODS: Three groups of five samples were evaluated. Group 1 (G1), machined titanium abutments, group 2 (G2), premachined cast straight abutments that cast with gold-palladium, and group 3 (G3), premachined angled cast abutments that cast with the same alloy, were angled before casting. Each abutment was torque to 24 Ncm according to the manufacturer's instructions and detorqued five times. The means of detorquing and torquing values in all groups were recorded. The mean of detorque in each group as a percentage of the toque value was calculated. The data for all groups were compared and calculated using analysis of variance (ANOVA) and t-test. RESULTS: Mean detorque values in G1, G2, and G3 were 88.1 ± 1.69, 93.1 ± 2.68, and 80.9 ± 4.95%, respectively. The ANOVA showed significant differences in mean of applied detorque (p < .001) and torque (p = .06) tightening among different groups. G2 had significantly greater detorque values (p < .05). No significant differences were found between G1 and G2. Surprisingly, abutment screw fracture occurred in three samples of G3. CONCLUSIONS: G3 showed significant percentage torque reduction (p < .05) and exhibited abutment screw fracture during evaluation. G2 presented the lowest torque reduction. Screw fracture occurred only in G3.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".