A Restoratively Driven Ridge Categorization, as Determined by Incorporating Ideal Restorative Positions on Radiographic Templates Utilizing Computed Tomography Scan Analysis
Bibliographic record
Abstract
BACKGROUND: The introduction of implants into the field of dentistry has revolutionized the way we evaluate edentulous ridges. In an attempt to evaluate the deficient edentulous ridge, numerous classification systems have been proposed. Each of these classification systems implements a different approach for evaluating and planning treatment for the ridge deficiency. PURPOSE: The purpose of the present investigation was to propose a restoratively driven ridge categorization (RDRC) for horizontal ridge deformities based on an ideal implant position as determined through implant simulation, utilizing computed tomography (CT) scan images. MATERIALS AND METHODS: Radiographic templates were developed to capture the ideal restorative tooth position. Measurements were performed using CT scan software in a cross-sectional view and by virtual placement of a parallel-sided implant with a 3.25-mm diameter. RESULTS: Edentulous ridges were divided into five groupings: Group I, simulated implants with at least 2 mm of facial bone, accounted for 19.4% of ridges; Group II, simulated implant completely surrounded by bone, with less than 2 mm of facial plate thickness, accounted for 10.4% of ridges; Group III, wherein dehiscences are detected but no fenestrations are present, accounted for 33.3% of ridges; Group IV, wherein fenestrations are detected but no dehiscence is present, accounted for 6.3% of ridges; and Group V, wherein both dehiscences and fenestrations are present, accounted for 30.6% of ridges. CONCLUSION: The use of RDRC indicates that a high number of cases in the maxillary anterior area would require augmentation procedures in order to achieve ideal implant placement and restoration.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".