Technical Accuracy of Printed Surgical Templates for Guided Implant Surgery with the co<scp>D</scp>iagnosti<scp>X</scp><sup>TM</sup> Software
Bibliographic record
Abstract
BACKGROUND: Printing of templates for guided surgery represents an alternative to laboratory manufactured templates. PURPOSE: To determine the technical accuracy of a virtually designed and printed surgical template for guided implant surgery based on a surface scan of a cast model using the coDiagnostiX™ software. MATERIALS AND METHODS: Cast models and the virtual planning data of nine patients receiving guided implant surgery with the coDiagnostiX software were analyzed. The original cast models were equipped with three titanium pins and scanned with a three-dimensional scanner. The scans were uploaded in the coDiagnostiX software and the virtual surgical templates were designed including the sleeves at their original positions. After printing the surgical templates, the sleeve positions were determined by optical scanning, and deviations were calculated and compared with the virtual positions of the sleeves. RESULTS: The sleeves showed a mean three-dimensional deviation of 0.22 mm (range: 0.07-0.38 mm) in the center of the sleeve top, 0.24 mm (range: 0.08-0.36 mm) in the center of the sleeve bases and a mean angular deviation of 1.5° (range: 0.4°-3.3°) compared with the virtual positions. CONCLUSIONS: A high accuracy can be achieved using printed templates for guided implant surgery, by taking into account all sources of inaccuracies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".