A novel conversion method for radiographic guide into surgical guide
Bibliographic record
Abstract
BACKGROUND: The study proposed a novel method for converting a radiographic guide into a surgical guide and evaluated its accuracy. MATERIALS AND METHODS: Radiographic guide was reformed with the addition of index rods for geometric conversion method (GCM). Planning implants were projected on geometric projection planes, and the implant positions were measured. The radiographic guide was converted into surgical guide using a generic bench drill machine with GCM data. Two experiments were designed to validate the GCM. (1) In vitro test: Twenty implants were placed on five edentulous dental models by using the GCM (group 1) and Stereolithography (SLA) method (group 2), respectively. The deviations of planned and placed implant were calculated, and the precision error (PE) value was calculated to evaluate the stability of the GCM and SLA. (2) In vivo test: Nine edentulous subjects were selected for clinical implant surgery with the GCM guide. Two level of the index rods of radiographic guides were prepared for surgical guides forming. The differences between the planned and actual implants were calculated in implant head, apex, and angulation. RESULTS: The in vitro test revealed no significant differences in the planned and placed angulations between groups 1 and 2 (P > .05). The PE was not significantly different between groups 1 and 2 (P > .05). The in vivo test revealed a successful treatment of the subjects, and 16 implant sites were evaluated. The results indicated that GCM guide could achieve the three-dimensional (3D) offset deviations of 1.03 ± 0.27 mm and 1.17 ± 0.24 mm at the implant head and apex, respectively, and 1.37° ± 0.21° for the 3D angulation. CONCLUSION: The novel method for converting a radiographic guide into a surgical guide appears accurate and stable compared with SLA.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".