Evaluation of masticatory performance and patient satisfaction for conventional and 3D-printed implant overdentures: a randomized crossover study
Bibliographic record
Abstract
BACKGROUND: This crossover clinical study aimed to evaluate and compare masticatory performance and patient satisfaction for patients rehabilitated with conventional heat-cured acrylic resin and 3D-printed mandibular implant overdentures retained with bar attachment. MATERIALS AND METHODS: Sixteen completely edentulous healthy participants received new conventional dentures. In the mandible, four interforaminal implants were inserted. Following the stage of osseointegration, the bar was constructed in a trapezoidal configuration. Each patient randomly received the following overdentures using a crossover design: (1) conventional heat-cured acrylic resin overdenture and (2) 3D-printed overdenture (developed by scanning of mandibular conventional overdenture). The masticatory performance was assessed by conducting a two-colour mixing ability test at 5, 10, 20, 30, and 50 masticatory cycles. Moreover, the McGill Denture Satisfaction Questionnaire (MDSQ) was employed to assess patient satisfaction. Evaluation was performed after 3 months of using each overdenture. Paired sample t tests were used to compare the masticatory performance and MDSQ scores of patients for both prostheses. RESULTS: No significant difference in masticatory performance was reported between the two types of overdentures. Regarding patient satisfaction, only the esthetic aspect was significantly better for conventionally processed overdentures than for printed overdentures. Insignificant differences were observed regarding other MDSQ items between the two overdentures. CONCLUSION: Within this clinical study, 3D-printed implant overdentures showed promising results in terms of chewing efficiency and patient satisfaction compared to conventionally fabricated implant overdentures. TRIAL REGISTRATION: Retrospectively registered at www. CLINICALTRIALS: gov : NCT06148727.(28/11/2023).
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".