Application of surgical guide for pre-drilling for the successful placement of orthodontic mini-screws using CAD/CAM technology in two cases
Bibliographic record
Abstract
An increasing number of clinicians have been utilising orthodontic mini-screws as temporary anchorage devices (TAD) in their practices, but variable successful rates have been reported. Here, we introduce a practical approach to inserting mini-screws successfully. Using computer-aided design (CAD) and computer-aided manufacturing (CAM) technology, the surgical guide for pre-drilling was designed and fabricated and mini-screws were placed following pre-drilling holes in two cases. Two Ø2.0 × 10.0-mm mini-screws were inserted into the prepared holes in the mandibular buccal shelf (MBS) on both sides with a hand driver to distalise the lower molars for Class III correction. The treatment was done successfully, after 12 months of treatment in one case. Two Ø1.6 × 8.0-mm mini-screws were inserted into the prepared holes in the mandibular alveolar process in another case with congenital absence of lower right second premolar. One mini-screw was in the buccal alveolar process between the mandibular right canine and first premolar and the other in the lingual alveolar process between the mandibular right first premolar and second primary molar. The lower right molars would be protracted to close the space left after the extraction of the primary molar using the two mini-screws. The case was still in treatment.
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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) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".