Congruency of Stereo Lithographically Produced Surgical Guide Bases Made from the Same CBCT File: A Pilot Study
Bibliographic record
Abstract
PURPOSE: This is a pilot study evaluating the effect of the algorithms and production processes of four commercial manufacturers of stereolithographically produced surgical guide. MATERIALS AND METHODS: A singular Dicom file was used to produce six distinct duplicate dentures, which function as the base for surgical guides. The duplicate dentures were repeatedly fitted (n = 10) into an impression of the occlusal surface of the original scan appliance. The gaps between the incisal edge of teeth #8 and #9 and the corresponding imprints in the vinyl polysiloxane impression were photographed, digitally recorded, and measured in a blinded fashion. RESULTS: Nobel Biocare mean was 0.56 mm (range 0.49-0.65), I-dent mean was 0.57 mm (range 0.31-0.74), Materialise II mean was 1.12 mm (range 0.90-1.40), Blue Sky Bio II mean was 1.13 mm (range 0.93-1.35), Materialise I mean was 1.43 mm (range 1.21-1.86), and Blue Sky Bio I mean was 2.17 mm (range 2.06-2.34). The difference between the fit of the Nobel Biocare and the I-dent guide bases and the guide bases from Materialise and Blue Sky Bio is statistically significant (p < .05). CONCLUSION: The algorithms and production processes of the different manufactures do influence the congruency outcome of the produced surgical guide bases. Within the limits of this study, we were unable to produce a perfect fit, although some duplicate dentures showed minimal errors. The implications of the discrepancies need further study.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".