The Influence of the Tolerance between Mechanical Components on the Accuracy of Implants Inserted with a Stereolithographic Surgical Guide: A Retrospective Clinical Study
Bibliographic record
Abstract
BACKGROUND: The stereolithographic-guided surgery system involves a sequence of diagnostic and therapeutic events, and errors can arise at different stages. In these systems, one of the potentially clinically relevant errors may be the mechanical errors caused by the bur-guide gap due to the presence of a rotational allowance of the drills in the tubes. PURPOSE: The purpose of this retrospective clinical study is to determine if it is possible to reduce the total error by limiting the tolerance among the mechanical components and to evaluate its clinical incidence. MATERIALS AND METHODS: Sixty-six implants were inserted using the External Hex Safe® (Materialise Dental, Leuven, Belgium) system (Group A), and 71 implants were inserted using the same system with mechanical components modified to minimize the tolerance (Group B). Regarding only the angular deviation values, the t-test was used to determine the influence of reduced tolerance among the mechanical components on the accuracy values. RESULTS: t-Test showed that there is a statistically significant better accuracy with the modified system (Group B). CONCLUSIONS: Limiting the error that originates from mechanical components, total error could be statistically significantly reduced. Mechanical error is one of the most important source of error using External Hex Safe stereolithographic surgical guide.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".