Accuracy of Two Stereolithographic Surgical Templates: A Retrospective Study
Bibliographic record
Abstract
BACKGROUND: The use of computer software and stereolithography for dental implant therapy has significantly increased during the last few years. The aim of this study was to evaluate and compare the mean accuracy and maximum deviations values of dental implant placement using two stereolithographic (SLA) guide systems. MATERIALS AND METHODS: Twenty patients were selected and 227 implants were inserted using bone-, tooth- and mucosa-supported SLA surgical guides. Thirty-one guides, both single- and multiple-type, were used. Some of the single-type surgical guides were fixed with osteosynthesis screws. A postoperative computer tomography (CT) was performed and an iterative closest point algorithm was used to match the jaw of the CT preoperative with the jaw of the postoperative CT. Quantitative data of each group were described. The t-test was used to determine the influence of the utilization of the different types of SLA on accuracy values. RESULTS: t-Test demonstrated a better accuracy of the multiple-type guides in almost all deviation values when the mucosa-supported guides were considered. Regarding the bone-supported template, the single-type fixed group showed a better accuracy while the highest values of deviation were registered by the multiple-type guides. The single-type group showed a better accuracy when the tooth support was considered. CONCLUSIONS: The results of the present study indicated best accuracy of the single-type guide using a bone or tooth support. The multiple-type guide recorded the best accuracy data when the mucosa support was considered comparing either a fixed and a not-fixed single-type 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".