A Retrospective Cohort Study on Scan Quality of Implant Scanbodies Matched With <scp>CAD</scp> Libraries
Bibliographic record
Abstract
PURPOSE: To assess the effect of scanbody (SB)-type, edentulous site, and restoration-type on the scan quality of SBs used in the treatment of short-span edentulism. MATERIALS AND METHODS: The cohort consisted of SBs with different specifications connected to bone-level implants for intraoral digitalization in the fabrication of fixed restorations. SBs matched with library CAD files for digital implant position transfer into dental CAD software were enrolled in the study group. Intraoral implant digital records were categorically evaluated to assess the quality of SB scans. In statistical analyses, the chi-squared test was used to describe the clinical variables, and logistic regression models were constructed to reveal the association between the clinical variables and SB scan quality. RESULTS: A total of 243 SBs were eligible for scan quality evaluation. Scan quality did not differ statistically (p > 0.05) in the SB reference area, while texture in the representation of SB was significantly affected (p < 0.05) by the variables SB-type and edentulous-site. Cylindrically designed SBs without specific geometrical features presented remarkably higher risks for reduced scan quality in SB representation. CONCLUSION: SBs successfully aligned with library CAD files based on a software algorithm may not consistently present similar scan quality. Intraoral scanning of a SB is highly vulnerable with regard to scan deterioration in texture and geometry.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".