Enhancing Implant Prosthodontics: In Vitro Accuracy of Coded Healing Abutments on the Edentulous Lower Jaw
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the accuracy of conventional and digital impression-making and cast-fabrication using coded healing abutments on an edentulous mandibular model under in vitro conditions. MATERIALS AND METHODS: Our study investigated the accuracy of the On1 Concept (Nobel Biocare; Kloten, Switzerland) coded healing abutment system using conventional and digital workflows. The Conical Connection (CC) system (Nobel Biocare; Kloten, Switzerland) was used as the control group in both workflows. 10-10 open-tray impressions and intraoral scans were made from the reference model with each system. Models built from intraoral scans were additively fabricated, and open-tray impressions were poured with type-4 dental stone. The prepared models were digitized using a desktop scanner with an accuracy of 4 μm (E4, 3Shape; Copenhagen, Denmark) and superimposed on the reference scan. Four linear distances and root mean square (RMS) deviations were measured using metrology software. RESULTS: Five dimensions were measured using signed and absolute deviations, resulting in nine outcomes. RMS and diagonal deviations provided the most insight into overall model deviations. Mean RMS deviations were: 58.30 (14.95) μm for CC_conv, 47.66 (13.04) μm for On1_conv, 204.97 (37.40) μm for CC_dig, and 136.64 (13.49) μm for On1_dig. Significant differences were found between On1_conv vs. CC_dig, On1_conv vs. On1_dig, and CC_conv vs. CC_dig. Mean linear deviations between the molar positions were: 24.49 (58.20) μm for CC_conv, 87.46 (106.70) μm for On1_conv, -104.76 (125.83) μm for CC_dig, and 140.64 (190.56) μm for On1_dig. Significant differences were observed between On1_conv vs. CC_dig and CC_dig vs. On1_dig. CONCLUSIONS: Based on the RMS deviations, the conventional method is significantly more accurate at both implant and platform levels in the case of an in vitro edentulous lower jaw model. The RMS deviations of the implant analogs are smaller on the platform level with both conventional and digital methods.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".