Accuracy of BLX and BLT guided implants in the edentulous maxilla: an in vivo study
Bibliographic record
Abstract
Objectives: To determine the accuracy of dental implants placed using a dual scan CBCT protocol with a SLA 3D printed mucosa supported surgical guide using a flapless surgical approach with regard to sleeve position, type of implant, and regional location in the maxillary arch. Methods: Nine patients received 4 dental implants utilizing a fully guided, flapless approach with a mucosa supported SLA surgical guide with 3 fixation pins. Implants were immediately loaded using an attachment placed in an existing Maxillary complete denture. Accuracy of implant positions were evaluated by the Treatment Evaluation module of coDiagnostiX (DentalWings, Montreal, Canada). Statistical analysis was completed based on the sleeve position, type of implant, and regional location in the maxillary arch. Results: An average angular deviation of 3.0° was seen. An average 3D offset of 1.05 mm and 1.10 mm were seen at the base and tip of the implants respectively. A statistically significant difference was seen between BLT and BLX implants with respect to 3D offset of the implant platform. A statistically significant increase in average apical 3D offset of implants were seen in implants placed in posterior regions when compared to anterior regions. Conclusions: A fully guided, flapless approach using Straumann BLT or BLX implants demonstrated an angular accuracy within 3° and approximately 1 mm of 3D offset from pre-surgical planning. BLT implants were seen to have an increased degree of inaccuracy. Implants placed in posterior regions of the maxilla demonstrated a greater degree of inaccuracy than those placed in anterior regions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".