Complications of Computer‐Aided‐Design/Computer‐Aided‐Machining‐Guided (NobelGuide™) Surgical Implant Placement: An Evaluation of Early Clinical Results
Bibliographic record
Abstract
PURPOSE: The aim of this study was to evaluate early clinical results of computer-aided design (CAD)/computer-aided machining (CAM)-guided surgical implant placement (NobelGuide, Nobel Biocare, Yorba Linda, CA, USA) with focus on surgical and/or prosthetic complications, management, and prevention. MATERIALS AND METHODS: Thirteen patients rehabilitated between March 2003 and October 2006 with CAD/CAM-guided dental implants and immediate loading (NobelGuide, Nobel Biocare) were evaluated. The treatment planning and procedures were carried out in accordance to the system protocol. The complications encountered in this case series were classified and assessed according to early (planning and procedural - surgical; prosthetic) and late complications (surgical; prosthetic). RESULTS: The prosthetic complications outnumbered surgical complications both in the early and late treatment phases. The main early surgical complication was bony interference that prevented complete seating of the prostheses. Most of the late surgical complications were implant failures with an overall failure rate of 9%. Fracture of the carbon fiber framework prosthesis was the main late prosthetic complication. CONCLUSIONS: The NobelGuide system is a reliable treatment modality, but not without its complications. Strict adherence to the system protocol is the key prevention of complications.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".