Digital versus conventional surveying for partially edentulous arches: an evaluation of accuracy and time efficiency
Bibliographic record
Abstract
This diagnostic study evaluated the accuracy and time efficiency of digital surveying compared to the conventional method for partially edentulous arches.MATERIALS AND METHODS.Thirty Standard Tesselation Language (STL) files of partially edentulous arches were analyzed.Conventional surveying was performed on 3D-printed diagnostic casts, while digital surveying was conducted using CAD software (Dental Wings Inc., Straumann, Montreal, Canada).The path of insertion and removal, and determining factors (guiding planes, undercut areas, and reciprocation) were assessed.Sensitivity and specificity tests were used to measure accuracy.Sensitivity was defined as the proportion of true positives identified by both techniques, while specificity was measured as a percentage of true negatives compared with the conventional method.Accuracy was assessed as the ability to correctly differentiate true positives and negatives.The paired t-test (95% CI) compared the mean working time between the techniques.RESULTS.Agreement on reciprocation was 2.91 times higher in regions with a greater number of edentulous areas compared to those with fewer edentulous areas (P = .025).The agreement of guiding planes in tooth-supported abutments was 2.59 times greater than in distal extension cases (P = .031).Accuracy ranged from 0.73 to 0.85.The working time was significantly longer for the digital technique (P = .030).CONCLUSION.Both techniques demonstrated high levels of agreement, especially for reciprocation and guiding planes.The digital method exhibited accuracy ranging from good to very good; however, it required a longer working time compared to the conventional approach.[
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".