Deviations between Placed and Planned Implant Positions: An Accuracy Pilot Study of Skeletally Supported Stereolithographic Surgical Templates
Bibliographic record
Abstract
PURPOSE: The aim of the present study was to evaluate deviations between virtually planned and placed implants by the use of skeletally supported stereolithographic templates. MATERIALS AND METHODS: Ten consecutive patients were selected for virtual three-dimensional implant planning using the Facilitate(TM) software (Astra Tech AB, Mölndal, Sweden). Computer tomography images were obtained in the pre- and postoperative phase. Four deviation parameters (i.e. global, angular, depth, and lateral deviation) were defined and calculated between the planned and the placed implants, using the coordinates of their respective apical and coronal points. RESULTS: Deviations at the coronal positions appeared to be smaller (95% confidence interval: 0.15-1.0) as compared with apical positions (95% confidence interval: 0.14-1.1). But only the difference with regard to lateral measurements appeared to be statistically significant (p = .03). Except for depth (p = .01), no significant association between mesial or more distal locations could be detected concerning global (p = .07), lateral (p = .87), and angular (p = .56) values in mixed model analyses. Overall, there was a slight tendency for higher values for more distal locations. CONCLUSION: As slight deviations between planned and placed implants especially may occur even with skeletal-supported templates, the clinician should be aware not to overestimate advocated surgical safety by using static navigation tools.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".