Fully digital versus conventional workflow: Are removable complete overdentures equally good? A randomized crossover trial
Bibliographic record
Abstract
INTRODUCTION: Implant-supported removable complete overdentures (IODs) are a common treatment in case of edentulism and malfunctioning of the conventional denture. Manufacturing IODs in a conventional way (C-IODs) is time-consuming, but in a digital workflow, this can be done in three sessions. Digitally produced IODs (3D-IODs) are also more advantageous than C-IODs because lost or broken 3D-IODs can be swiftly reproduced as the digital design is always available. PURPOSE: To prove in a non-inferiority study, with a margin of 0.3 point per Oral Health Impact Profile-20 (OHIP-20) question, that IODs made according to a fully digital workflow (3D-IODs), function as good as C-IODs with respect to patient-reported outcome measures (PROMs). MATERIALS AND METHODS: This randomized crossover study included 36 fully edentulous patients who showed extreme resorption of the maxillary alveolar process, making denture retention difficult. After a maxillary bone augmentation and the installation of 4-6 implants, each patient wore both types of IOD for 1 year each, with the order reversed in two subsets of patients. The 3D-IODs and C-IODs were fabricated in advance for both jaws (at least two mandibular implants were already present). The OHIP-20 survey was performed at baseline, after 1 year (before the IOD switch), and after 2 years to determine patient satisfaction scores using a visual analog scale (VAS). The general health status was assessed using the Short Form (SF-36) questionnaire. RESULTS: Regarding the PROMs, patients preferred the 3D-IOD: the improvement on the overall OHIP scale (0-4), expressed as a mean, was 0.26 points greater than for the C-IOD (p < 0.001). This applied also to the VAS scale (1-100) with an increase of 7.37 points (p < 0.001). Regarding the SF-36 scale, only for the item "emotional well-being," the 3D-IOD scored significantly better (p = 0.033). CONCLUSION: Compared with conventionally fabricated C-IODs, fully digitally produced 3D-IODs resulted in significantly higher OHIP-20 and satisfaction scores.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".