Patient‐Specific 3D‐Printed Drill Guides for an Ovine Osteochondral Allograft Transplantation Model
Bibliographic record
Abstract
Proper alignment between donor and recipient cartilage in osteochondral allograft transplantation supports tissue integration and the formation of a stable articulating surface. This study evaluated the use of patient-specific 3D-printed drill guides to improve alignment in an ovine model of osteochondral allograft transplantation when used in place of a free-hand drilling technique. Fourteen female Arcott sheep underwent bilateral osteochondral allograft transplantation. Drill guides were used to transplant 51 grafts (6.5 mm diameter), while the freehand drilling technique was used to transplant 32 grafts. After a 9-month survival time, tissues were harvested. Cross-sectional confocal microscopy images and histological sections were used to quantify height differences between donor and recipient tissues. Grafts transplanted with the drill guides showed significantly reduced (p = 0.0005) height differences between donor and recipient tissues compared to the freehand drilling technique. However, the guide technique was associated with increased osteophyte development (p = 0.0180) and synovial inflammation (p = 0.0451). These findings demonstrate that patient-specific 3D-printed drill guides improve graft alignment in an ovine model of osteochondral allograft transplantation. Methodological improvements are proposed to minimize osteophyte formation and inflammation in future studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".