Computer‐assisted mosaic arthroplasty using patient‐specific instrument guides
Bibliographic record
Abstract
PURPOSE: Success of mosaic arthroplasty requires that the transplanted plugs be positioned to reconstruct the curvature and height of the original articular surface. This case report demonstrates how to achieve correct plug positioning using patient-specific instrument guides manufactured on a 3D printer. METHODS: Using a 3D computer model of bone and cartilage, the harvesting of plugs and their placement at the defect site was planned on the computer. Instrument guides were manufactured in thermoplastic on a 3D printer; the bottom surface of the guides fit to the contour of the knee and the top surface contained holes to precisely position the surgical instruments. The instrument guides were used on a young female patient to repair a large articular cartilage defect in the left knee. RESULTS: The patient showed an increased range of motion in the knee and also a decrease in pain and discomfort at her 2-year follow-up. A CT arthrogram at 2 years postoperative showed a smooth and appropriate contour of the reconstructed cartilage over the defect. CONCLUSIONS: Image-based preoperative planning and the use of patient-specific instrument guides can yield a good patient outcome without requiring optically tracked intraoperative guidance.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".