<scp>CAD</scp> / <scp>CAM</scp> versus traditional complete dentures: A systematic review and meta‐analysis of patient‐ and clinician‐reported outcomes and costs
Bibliographic record
Abstract
STATEMENT OF PROBLEM: Computer-aided design and manufacturing (CAD/CAM) have been increasingly used to enhance the patient and clinician experiences with removable complete dentures (CDs). Yet, evidence from systematic reviews is lacking to validate the clinical significance of these digital prostheses. PURPOSE: The purpose of this systematic review was to compare CAD/CAM CDs with the traditional ones in terms of patient and clinician-reported outcomes, post-insertion adjustment visits and costs. MATERIALS AND METHODS: An electronic search of four databases [Medline (Ovid), Embase, Scopus and Cochrane CENTRAL; last update: May 2022] was performed to retrieve clinical studies comparing CAD/CAM and traditional CDs. Two independent reviewers screened the articles, extracted data (methods and outcomes) and assessed risk of bias of the included studies. The following outcomes underwent meta-analysis (random-effects model): overall patient and clinician satisfaction, oral health-related quality of life (OHRQoL), number of post-insertion adjustment visits, as well as laboratory and total costs. RESULTS: This review included 11 studies. Meta-analysis revealed that CAD/CAM CDs are comparable to the traditional CDs in terms of overall patient satisfaction and OHRQoL. Clinician-reported data depended on the manufacturing technique: whereas milled CDs performed better than traditional CDs in terms of clinician satisfaction and number of adjustments, 3D printed and traditional CDs were similar. Fabrication of CAD/CAM CDs required significantly less laboratory and overall costs than the traditional CDs. CONCLUSIONS: There is some evidence showing that CAD/CAM CDs are at least comparable to traditional CDs. Further well-designed randomized clinical trials are needed to evaluate the performance of specific CAD/CAM approaches for manufacturing CDs, however.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".