Clinical Fitting and Adjustment Time for Implant‐Supported Crowns Comparing Digital and Conventional Workflows
Bibliographic record
Abstract
PURPOSE: The aim of this prospective cohort study was to investigate clinical and laboratory performance of implant-supported reconstructions comparing the digital to the conventional workflow. MATERIALS AND METHODS: Twenty study participants were treated in a cross-over design for single-tooth replacement in posterior sites, each with a customized titanium abutment plus computer-assisted design and computer-assisted manufacturing (CAD/CAM)-zirconia-suprastructure (test: digital workflow; n = 20) and a standardized titanium abutment plus PFM-crown (control: conventional pathway; n = 20). Evaluation of the 40 reconstructions included: 1) feasibility of laboratory cross-mounting of each abutment-crown-connection, and 2) assessment of adaptation time for clinical adjustments of interproximal and occlusal surfaces. Statistical analyses were performed using the exact Wilcoxon rank sum tests. RESULTS: Laboratory cross-mounting was feasible for three reconstruction pairings revealing a 15% vice versa transfer success rate. All implant crowns could be provided successfully within two clinical appointments, independently of the workflow used. The mean clinical adjustment time was significantly lower (p < .001) for test reconstructions from the digital workflow with 2.2 min (standard deviation [SD] ± 2.1) compared with the ones from the conventional pathway with 6.0 min (SD ± 3.9). CONCLUSIONS: The digital workflow was almost threefold more efficient than the established conventional pathway for fixed implant-supported crowns. Clinical fitting could be predictably achieved with no or minor adjustments within the digital process of intraoral scanning plus CAD/CAM technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".