Virtuelle Planung bei der epithetischen Orbitaversorgung
Bibliographic record
Abstract
Optimal positioning of bone-anchored implants in the treatment of patients with orbital prosthesis is challenging. The definition of implant axis as well as the positioning of the implants is important to prevent failures in prosthetic rehabilitation in these patients. We performed virtual planning of enossal implants at a base of a standard fan beam CT scan using the software CoDiagnostiX™ (DentalWings, Montréal, Canada). By 3D-printing a surgical guide for drilling and implant insertion was manufactured (Med-610™, Stratasys, Rehovot, Israel). An orbital exenteration was performed in a patient after shrinkage of the eyelids 20 years after enucleation and radiation of the orbit due to rhabdomyosarcoma. 4 Vistafix-3 implants (Cochlear™, Cochlea, Centennial, USA) were primarily inserted after resection with the help of the 3D-surgical guide. Prosthetic rehabilitation could be achieved as preplanned to a predictable result. The individual prosthesis of the orbit showed good functional and esthetic outcome. The virtual 3D-planning of endosseous implants for prosthetic orbital and periorbital reconstruction is easy to use and facilitates optimal placement of implants especially in posttherapeutically altered anatomic situations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".