The Accuracy of an Implant Impression Technique Using Digitally Coded Healing Abutments
Bibliographic record
Abstract
BACKGROUND: A healing abutment (Encode) provided with digitally coded information on length and diameter on the top was launched in 2007. So far, no study has evaluated working cast fabrication using impressions of the coded abutments and analogue placement using a robot technique. PURPOSE: To compare the accuracy of implant analogue placement in working casts using a robot technique and an impression of Encode healing abutments, with the traditional technique. MATERIALS AND METHODS: One acrylic master model was fabricated, provided with two groups of three implant analogues. Encode healing abutments were mounted on the test side and conventional pickup impression copings were inserted on the control side. Fifteen impressions were made with a vinylpolysiloxane material. Implant analogues were placed by a robot on the test side. The center point of each implant analogue fitting surface was measured with a laser measuring machine in the x-, y-, and z-axis, as were also the angular direction of the center axis and the position of the antirotational hex. Two-way analysis of variance was performed using SPSS 17.0; the statistical significance was set at p < .05. RESULTS: Mean center point deviation for the test and control side was 37.4 µm versus 18.5 µm (p = .001) in the x-axis, 47.3 µm versus 13.9 µm (p < .001) in the y-axis, and 35.0 µm versus 15.1 µm (p < .013) in the z-axis. Mean angle error was 0.41 degrees for the test and 0.14 degrees for the control side (p < .001). Mean rotation of the hexagon was 2.88 degrees for the test side and 1.82 degrees for controls (p < .001). CONCLUSIONS: Both conventional and robot technique presented low levels of displacement of the implant analogues in all casts. The test technique was less precise, but the difference in accuracy was small, and both techniques are precise enough for single crowns and short-span, implant-supported fixed partial prostheses.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".