Delineamento digital de arcos parcialmente desdentados para planejamento de próteses parciais removíveis: estudo de acurácia
Bibliographic record
Abstract
This diagnostic study aimed to analyze the diagnostic accuracy of the digital design of the conventional design of partially edentulous arches for planning removable partial dentures (RPD), using the Roach technique. The sample consisted of 143 dental faces present in 30 models of partially edentulous arches in the STL (Tesselation Standart Language) format, which were submitted to two methods: conventional design (Group DC), from the impression of the models and digital (Group DV), with the STL files of the model, imported into the Dental Wings software (DWOS v.9.06, Montreal – Canada. A specific form was designed to collect the following items: guide planes, retentive areas, reciprocity, and design time. The accuracy and coincidence between the evaluated items and the arch's location and the number of prosthetic spaces between the conventional and digital designs were evaluated. The result showed good accuracy for the guide plane factors (0.79), undercut areas (0. 85), and reciprocity (0.73). The time required to perform the digital design was shorter (1.64 ± 0.54 minutes) than that required for the conventional method (2.20 ± 0.89 minutes) (p< 0.001). The coincidence The probability of results in the reciprocity analysis between the digital and conventional design was higher in superior models compared to inferior models. However, the greater the number of prosthetic spaces, the smaller the coincidence between the methods for the guiding plane and retentive area factors. Conclusion: The digital method of design showed good accuracy compared to the conventional one. In this sense, the visualization of the retentive areas by a color gradient facilitates the identification of the determining factors, making the digital technique faster and more objective for the diagnosis of the need to prepare the pillar elements in RPD.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".