An Image‐Guided System Based on Custom Templates: Case Reports
Bibliographic record
Abstract
BACKGROUND: With the use of computer-assisted surgery and other modern imaging technologies, the surgeon's procedures have been modified. PURPOSE: The purpose of these case reports is to show the clinical predictability of dental implant placement using an image-guided system. MATERIAL AND METHODS: An acrylic template is made on the patient model. After computed tomographic examination, a treatment plan is established with appropriate software. To fabricate the surgical template, it is necessary to use a dedicated drilling machine. The first osteotomy is achieved through the template with a 2 mm twist drill. The template is then removed, and the osteotomy is completed, followed by implant placement with standard clinical procedures. RESULTS: An excellent predictability was observed between the planned implants and those placed in the two maxillary cases presented: a full upper screw-retained bridge and two single units. CONCLUSIONS: This new technology improves the treatment outcome and optimizes the surgical procedure.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".