Stereoscopic Technique for Conversion of Radiographic Guide into Implant Surgical Guide
Bibliographic record
Abstract
PURPOSE: The aim of this study was to develop and evaluate a new stereoscopic technique for conversion of radiographic guide into surgical guide for dental implant placement. MATERIALS AND METHODS: Ten partially dentate patients requiring 18 implants for tooth replacement were recruited. Radiographic guides were modified with the addition of index rods for double computed tomography scanning. Implant positions were planned with implant planning software, and the stereoscopic angulations were measured. The radiographic guides were converted into surgical guides using either a generic bench drill (Group A, n = 9) or a milling machine (Group B, n = 9). Stereolithographic surgical guides were also made for three patients (Group S, n = 5). Differences between the planned and actual angulations were tested by pair-sample t-test. Difference of mean angle deviation among groups was tested by Brown-Forsythe test. Differences were considered significant if p < .05. RESULTS: Eighteen implant sites were successfully treated with the converted surgical guides. The mean angle deviation of Group A (1.3 ± 0.6°) was significantly greater than Group S (0.4 ± 0.6°), while no differences were found between Group B (0.9 ± 0.3°) and Group S. The linear error was greatest in Group A with 1.5 mm at the head and 1.8 mm at the apex of the implant. CONCLUSIONS: The use of this new stereoscopic technique appears to be an acceptable alternative method for converting radiographic guide into surgical guide.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".