Clinical and laboratory passive fit assessment of implant‐supported zirconia restorations fabricated using conventional and digital workflow
Bibliographic record
Abstract
BACKGROUND: Long-term success of implant-supported restorations can be affected by the accuracy of the prosthodontic workflow which may differ between conventional and digital techniques. PURPOSE: The purpose was to compare the fit of two-implant-supported restorations, fabricated using conventional and digital workflows and to assess the influence of distance and angulation between the implants on the passive fit of the prosthesis. The SR test was selected to evaluate the fit of two-implant-supported zirconia restorations. MATERIALS AND METHODS: Forty-eight zirconia two-implant-supported restorations were fabricated according to conventional (group C, n = 24) and digital (group D, n = 24) workflows. The SR parameter was calculated as a difference of rotation angles of each screw in passive and nonpassive situations. SR values between groups C and D were compared by performing measurements intraorally, on master and control casts. RESULTS: SR intraorally in group C (16.25 ± 15.52°) was higher than it was in group D (13.85 ± 10.78°), but the difference was not statistically significant (P = .557). While measuring SR on the master cast, group C SR (6.04 ± 7.43°) had lower values than group D (13.12 ± 13.86°) (P = .0039). No statistically significant correlations were found between SR measurements and inter-implant distance or angulation. Restorations with inter-implant angle higher than 10° differed significantly from those with less than 10° angulation. CONCLUSIONS: Digital restorations had a better fit on the control cast, which was used as a reference in this study. Angulation of more than 10° between the implants could negatively affect the passive fit of the digitally fabricated restorations intraorally.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".