Precision of Fit to Implants: A Comparison of Cresco™ and Procera® Implant Bridge Frameworks
Bibliographic record
Abstract
BACKGROUND: The Cresco™ (Astra Tech AB, Mölndal, Sweden) method aims to reduce the inevitable distortions when cast metal frameworks for implant-supported prostheses are fabricated. However, limited data are available for the precision of fit for this method. PURPOSE: To measure and compare the precision of fit of Cresco- and computer numeric controlled (CNC)-milled metal frameworks for implant-supported fixed complete prostheses. MATERIALS AND METHODS: Two groups of frameworks were fabricated according to the Cresco method, either in titanium (Cresco-Ti, n = 10) or in a cobalt-chrome alloy (Cresco-CoCr, n = 10). A third group comprised CNC-milled titanium frameworks (Procera® Implant Bridge [PIB], Nobel Biocare AB, Göteborg, Sweden), made from individual model/pattern measurements (PIB, n = 5). Measurements of fit were performed by means of a coordinate measuring machine linked to a computer. The collected data on distortions were analyzed. RESULTS: Overall, a maximal three-dimensional range of center point distortion of 279 µm was observed for measured frameworks. The framework width (x-axis) decreased for Cresco-CoCr, but increased in Cresco-Ti and PIB; Cresco-CoCr compared to Cresco-Ti (p = .0002) and Cresco-CoCr compared to PIB (p < .0001). In vertical dimension (z-axis), less distortions were present in PIB compared to Cresco-CoCr (p = .0007) and in PIB compared to Cresco-Ti (p < .0001). CONCLUSIONS: None of the frameworks presented a perfect, completely "passive fit" to the master. Although the direction of distortions varied, the horizontal distortions were of similar magnitudes. However, the PIB frameworks had statistical significant less vertical distortions as compared to the Cresco groups.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".