Early Postoperative CT Scan Provides Prognostic Data on Clinical Outcomes of Fresh Osteochondral Transplantation of the Knee
Bibliographic record
Abstract
Background: There is a lack of information regarding the ability of imaging studies to predict clinical outcomes after fresh osteochondral allograft (FOCA) transplantation of the knee. Purpose: To determine the value of computed tomography (CT) scans to predict the clinical outcome of FOCA transplantation using the assessment computed tomography osteochondral allograft (ACTOCA) score. Study Design: Cohort study; Level of evidence, 3. Methods: We prospectively collected data from all consecutive patients who underwent FOCA transplantation for osteochondral knee lesions at one institution between August 2017 and August 2019. All patients were followed up for a minimum of 2 years. CT scans performed 6 months after surgery were evaluated by a musculoskeletal radiologist using the ACTOCA scoring system. The radiologist was blinded to the patient’s medical history. Clinical outcomes were assessed preoperatively and at 12 and 30 months postoperatively using the International Knee Documentation Committee (IKDC) score, the Kujala score, the Tegner activity scale, and the Western Ontario Meniscal Evaluation Tool (WOMET) score. Results: A total of 38 cases were included. The ACTOCA score at 6 months after surgery showed a statistically significant correlation with clinical results at 12 and 30 months. The correlation was better at 30 months, showing a high negative correlation with the IKDC score (−0.663) and a moderate negative correlation with the Kujala, WOMET, and Tegner scores (−0.593; −0.547, and −0.593, respectively) ( P < .001). Conclusion: A statistically significant correlation between the mean ACTOCA score on CT scans at 6 months and the clinical results measured by the IKDC, Kujala, WOMET, and Tegner scores at 30 months confirmed the predictive value of the ACTOCA score for use in clinical practice.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".