Monolithic implant‐supported lithium disilicate (LS2) crowns in a complete digital workflow: A prospective clinical trial with a 2‐year follow‐up
Bibliographic record
Abstract
Abstract Background The technical development of digital processing allows the production of anatomically full‐contoured implant‐supported restorations. Purpose The aim of this prospective clinical trial was to analyze the treatment concept of monolithic lithium disilicate (LS2) single‐unit restorations in a complete digital workflow. Material and Methods Forty‐four patients were restored with 50 screw‐retained monolithic implant LS2 crowns bonded to pre‐fabricated titanium abutments on soft tissue level implants (Institut Straumann AG, Basel, Switzerland) in premolar and molar sites. All implant restorations were digitally designed after intraoral optical scanning (IOS) and CAD/CAM‐processing without physical model situations. Study participants were clinically and radiographically examined based on an annually performed follow‐up. The “Functional Implant Prosthodontic Score” (FIPS) was applied for objective outcome assessment after 2 years of loading. Five variables were defined for FIPS evaluation, resulting in a maximum score of 10 per implant restoration. Descriptive statistics were calculated for mean scores standard deviations, medians, and Q 25 –Q 75 . Results All patients could be successfully treated within two clinical appointments. No clinical modifications were necessary for the seating of the monolithic crowns, neither for interproximal nor occlusal sites. The implant LS2 restorations demonstrated survival rates of 100% without any technical or biological complications after 2 years. The mean total FIPS score was 7.7 ± 1.0, ranging from 6 to 10. Conclusions CAD/CAM‐produced monolithic implant crowns out of LS2 in a complete digital workflow seem to be a feasible treatment concept for the rehabilitation of single‐tooth gaps in posterior sites under mid‐term observation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.004 | 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".