Optical Three‐Dimensional Scanning Acquisition of the Position of Osseointegrated Implants: An in vitro Study to Determine Method Accuracy and Operational Feasibility
Bibliographic record
Abstract
PURPOSE: Manufacturing complex prosthetic framework on osseointegrated implants requires precision at every step of execution. The purpose of this study was to verify the possibility of applying the technology of image acquisition to determine the spatial position of osseointegrated implants. MATERIALS AND METHODS: An optical three-dimensional scanning technique was employed: its measurement systematic error (bias) was calculated by comparing the results with the detection on a coordinates measuring machine. Measurements were carried out on master casts by doing an in vitro simulation of intraoral conditions. RESULT: This study showed that the bias error value of the three-dimensional optical acquiring system was situated between 14 and 21 microm. CONCLUSION: As far as the accuracy is concerned, it seems possible to use the three-dimensional image acquisition technology as a valid alternative to traditional impression-making procedures. However, the bias levels obtained in this in vitro study will have to be confirmed in a clinical trial.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".