Expediting the Rehabilitation of Severely Resorbed Ridges Using a Combination of CAD-CAM and Analog Techniques: A Case Report
Bibliographic record
Abstract
With the life expectancy increasing, there is a growing need for prosthetic dental treatments to restore the oral health, function, and quality of life of edentulous patients. Presently, only a few articles are available describing the oral rehabilitation of patients with severely resorbed ridges with milled complete dentures. This clinical case report provides a straightforward protocol consisting of a combination of analog and digital techniques for the rehabilitation of edentulous patients with severely resorbed ridges with milled fixed and removable complete dentures. This technique permits the minimization of the number of appointments, improves patient comfort, allows for the digital archiving of important clinical data, and permits the manufacture of prostheses with improved mechanical properties. These favorable outcomes were achieved by using the patient's existing PMMA complete denture as a custom tray for a final impression with light-bodied Polyvinylsiloxane. Subsequently, the resulting models were digitized, and a digital complete denture was designed and manufactured in an expedited manner using CAD-CAM techniques. Therefore, this case report highlights the potential of CAD/CAM technology to predictably restabilize oral functions and improve patients' quality of life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.004 |
| 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".