Implant Overdentures: Dental Students’ Performance in Fabrication, Denture Quality, and Patient Satisfaction
Bibliographic record
Abstract
The purpose of this study was to evaluate dental students' performance when fabricating a mandibular two-implant overdenture (OD) as compared to conventional dentures (CD) and to determine if these prostheses were successful. Twenty students and twenty patients were divided into two groups: complete denture group (CDG) and maxillary denture and two-implant OD group (ODG). Students' progress was evaluated at each appointment as they were given a clinical assessment score (CAS), which varied from 1 (unacceptable, needs to repeat procedure) to 4 (acceptable, no errors). The success of the prosthesis was evaluated by the patients using a visual analog scale (VAS) and an expert (a prosthodontist) using a denture quality assessment (DQA) form. Performance for both groups was not statistically different across all eight appointments (CDG 3.16 versus ODG 3.25; p=0.46). Patients with ODs reported greater stability with their dentures (p=0.048) and greater ability to chew than patients with CDs (p=0.03). There were no differences between the groups in terms of expert appraisal (ODG 71.1 versus CDG 67.5; p=0.59). The performance of dental students when fabricating a two-implant OD is thus not different from that of a CD. Students can successfully fabricate a two-implant OD as perceived by both patients and prosthodontists.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".