Accuracy of computer‐guided implantation in a human cadaver model
Bibliographic record
Abstract
OBJECTIVES: To examine the accuracy of computer-guided implantation using a human cadaver model with reduced experimental variability. MATERIALS AND METHODS: Twenty-eight (28) dental implants representing 12 clinical cases were placed in four cadaver heads using a static guided implantation template. All planning and surgeries were performed by one clinician. All radiographs and measurements were performed by two examiners. The distance of the implants from buccal and lingual bone and mesial implant or tooth was analyzed at the apical and coronal levels, and measurements were compared to the planned values. RESULTS: No significant differences were seen between planned and implanted measurements. Average deviation of an implant from its planning radiograph was 0.8 mm, which is within the range of variability expected from CT analysis. CONCLUSIONS: Guided implantation can be used safely with a margin of error of 1 mm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".