Image-Guided Techniques Improve the Short-Term Outcome of Autologous Osteochondral Cartilage Repair Surgeries
Bibliographic record
Abstract
OBJECTIVE: Autologous osteochondral cartilage repair is a valuable reconstruction option for cartilage defects, but the accuracy to harvest and deliver osteochondral grafts remains problematic. We investigated whether image-guided methods (optically guided and template guided) can improve the outcome of these procedures. DESIGN: Fifteen sheep were operated to create traumatic chondral injuries in each knee. After 4 months, the chondral defect in one knee was repaired using (a) conventional approach, (b) optically guided method, or (c) template-guided method. For both image-guided groups, harvest and delivery sites were preoperatively planned using custom-made software. During optically guided surgery, instrument position and orientation were tracked and superimposed onto the surgical plan. For the template-guided group, plastic templates were manufactured to allow an exact fit between template and the joint anatomy. Cylindrical holes within the template guided surgical tools according to the plan. Three months postsurgery, both knees were harvested and computed tomography scans were used to compare the reconstructed versus the native pre-injury joint surfaces. For each repaired defect, macroscopic (International Cartilage Repair Society [ICRS]) and histological repair (ICRS II) scores were assessed. RESULTS: Three months after repair surgery, both image-guided surgical approaches resulted in significantly better histology scores compared with the conventional approach (improvement by 55%, P < 0.02). Interestingly, there were no significant differences found in cartilage surface reconstruction and macroscopic scores between the image-guided and the conventional surgeries.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".