Analysis of Defect Size and Ratio to Condylar Size With Respect to Outcomes After Isolated Osteochondral Allograft Transplantation
Bibliographic record
Abstract
BACKGROUND: Osteochondral allograft transplantation (OCA) is a successful knee joint preservation technique; however, the effects of defect size and defect size:condyle ratio (DSCR) are poorly understood. PURPOSE: To quantify clinical outcomes of isolated OCA of the knee based on defect size and DSCR. STUDY DESIGN: Cohort study; Level of evidence, 3. METHODS: Data from patients who underwent OCA of the knee without major concomitant procedures by a single surgeon were analyzed at a minimum follow-up of 2 years. Osteochondral defect size was measured intraoperatively, and femoral condyle size was measured with preoperative imaging. Patient-reported outcomes, reoperations, and survival rates were analyzed per defect size and DSCR, comparing males and females and patients <40 and ≥40 years old. RESULTS: , P = .03) and higher DSCR (mean 0.30 ± 0.25 vs 0.19 ± 0.06, P = .05) when compared with nonfailures. Failures among patients <40 years old were not significantly associated with defect size or DSCR ( P > .05) as compared with nonfailures. CONCLUSION: Patients undergoing isolated OCA transplantation demonstrated significant clinical improvements and a graft survival of 88.2% at 5.2 years. Failures overall were associated with a larger DSCR, and failures among patients ≥40 years old with a larger DSCR and larger defect size. Increasing defect size among males was positively correlated with some improved outcomes, although this was not maintained in analysis of the DSCR, suggesting similar prognosis after OCA regardless of sex. CLINICAL RELEVANCE: Failed osteochondral allografts are associated with larger defect sizes and defect:condyle ratios in this study, providing additional information to surgeons for appropriate patient consultation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".