Accuracy of the match between cone beam computed tomography and model scan data in template‐guided implant planning: A prospective controlled clinical study
Bibliographic record
Abstract
BACKGROUND: Template-guided implant placement is a method for optimal implant positioning from a prosthetic and surgical viewpoint. The treatment planning is based on three-dimensional X-ray data and model scan data, as well as on prosthetic planning (set-up). These data are matched (superimposed) with the aid of an X-ray template or by manual matching without special referencing. PURPOSE: The objective of this prospective controlled clinical study was to determine and compare the accuracy of the match with and without an additional X-ray template. MATERIALS AND METHODS: The DICOM data of the cone beam computed tomography (CBCT) were converted into surface data sets and then superimposed on model scan data using three different methods (manually, based on an X-ray template, or semi-automatically with computer assistance). The mean deviations between these results of these matching methods were investigated. RESULTS: The procedures achieved a matching accuracy of 0.2 mm on average. This corresponds to the resolution of the CBCT (0.2 voxels). Further studies are necessary to verify the procedure even for patients with few (0-4) residual teeth. CONCLUSION: In the presence of a sufficient number of residual teeth, the manual matching of model scan data with CBCT data is sufficiently accurate for implant planning and template-guided implementation. The results of the present study suggest that X-ray templates can be dispensed with saving the patient a substantial amount of time and money.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".