Three‐Dimensional Image Correlation Analyses for Strains Generated by Cement and Screw‐Retained Implant Prostheses
Bibliographic record
Abstract
PURPOSE: This study aimed to measure and compare strains generated by splinted implant crowns retained by cement or screws for two implants with applied load. MATERIALS AND METHODS: A stereolithic resin model was printed using computed tomography data from a patient missing all mandibular molar teeth. Two 4 × 6 mm implants were consecutively placed in the left side. One set of splinted cement and screw-retained crowns were made to fit the two implants. Image correlation technique was used for full-field measurement of strains using an image correlation software and two synchronized high-resolution digital cameras. A random dot pattern was applied to the model surface. Cameras recorded changes in random dot patterns as prostheses were loaded up to 400 N in vertical and oblique directions using a universal testing machine. Testing was repeated three times for cement and screw-retained prostheses. An image correlation algorithm used the dot pattern to define correlation areas or virtual strain gauge boxes. Three-dimensional coordinates of gauge box centers were determined for each recorded photograph and used to calculate strains. Strain distribution data were compared for major, minor, and von Mises strains for each loading condition, as well as peak and average strains for the field of view using an analysis of variance (α = 0.05). RESULTS: Patterns and magnitudes of strain for cement- and screw-retained splinted crowns were similar under vertical loading. Neither peak nor mean strains were significantly different for the two retention methods. For oblique loading, peak strains were lower for the screw-retained crowns; however, there were no statistically significant differences between the two groups when strains were averaged throughout the entire field of view. CONCLUSIONS: Cement retention did not improve the magnitude of transferred strains for splinted implant crowns using either loading 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".