Screw Preloads and Measurements of Surface Roughness in Screw Joints: An In Vitro Study on Implant Frameworks
Bibliographic record
Abstract
BACKGROUND: With the development of milled titanium implant frameworks, new surfaces that have not previously been studied are now being used in screw joints. PURPOSE: The aims of the present study were to compare the preload produced in screw-retained titanium and gold alloy frameworks and the preload for titanium frameworks before and after the application of veneers. Another aim was to try to relate the surface roughness of the screw joints to variations in preload. MATERIALS AND METHODS: Ten identical titanium and five gold alloy frameworks were fabricated. The gold screws were tightened to 10 Ncm. Preload measurements were made for the gold alloy frameworks and before and after the porcelain or acrylic resin veneers had been applied to the titanium frameworks. Surface roughness measurements were made after preload measurements on the screw joint surfaces of the titanium frameworks and corresponding gold screws. RESULTS: The preloads for the titanium and gold alloy frameworks were similar. Preload in both types of frameworks decreased after repeated torques (p<.05-.01) but was unaffected by the application of veneering materials to the titanium frameworks (p>.05). No relationship (p>.05) between preload and surface roughness characteristics was observed. Loaded titanium framework screw sites, however, had lower mean S(a) values than unloaded sites (p<.001), whereas the surfaces of loaded gold screws had higher mean S(a) values compared with the surfaces of control gold screws (p<.05-.001). CONCLUSION: When using gold screws, milled titanium frameworks have preloads similar to those of gold alloy frameworks and preloads for both decrease after repeated tightening. The preload was similar before and after the veneering of the titanium frameworks. Unloaded milled titanium screw sites had rougher surfaces than loaded, and loaded gold screws had rougher surfaces than unloaded. However, no correlation between screw joint surface and preload was observed for veneered titanium frameworks.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".