Effect of length and location of edentulous area on the accuracy of prosthetic treatment plan incorporation into cone‐beam computed tomography scans
Bibliographic record
Abstract
BACKGROUND: Effects of length and location of the edentulous area on the accuracy of prosthetic treatment plan incorporation into cone-beam computed tomography (CBCT) scans has not been investigated. PURPOSE: To evaluate the effect of length and location of the edentulous area on the accuracy of prosthetic treatment plan incorporation into CBCT scans using different methods. MATERIAL AND METHODS: Direct digital scans of a completely dentate master model with removable radiopaque teeth were made using an intraoral scanner, and digital scans of stone duplicates of the master model were made using a laboratory scanner. Specific teeth were removed to simulate different clinical situations and their CBCT scans were made. Surface scans were registered onto the CBCT scans. Radiographic templates for each clinical situation were also fabricated and used during CBCT scans of the master models. Using metrology software, three-dimensional (3D) deviation was measured on standard tesselation language (STL) files created from the CBCT scans against an STL file of the master model created from a CBCT scan. Statistical analysis was done using the MIXED procedure in a statistical software and Tukey HSD test (α =.05). RESULTS: The interaction between location and method was significant (P = .009). Location had no significant effect on registration methods (P > .05), but on the radiographic templates (P = .011). Length of the edentulous area did not have any significant effect (P > .05). CONCLUSIONS: Accuracy of digital image registration methods was similar and higher than that of radiographic templates in all clinical situations. Tooth-bound radiographic templates were significantly more accurate than the free-end templates. The results of this study suggest using image registration instead of radiographic templates when planning dental implants, particularly in free-end situations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".