Bibliographic record
Abstract
The parameters to be considered in the selection of a cartilage repair strategy are: the diameter of the chondral defect; the depth of the bone defect; the location of the defect (weight bearing); alignment. A chondral defect less than 3 cm in diameter can be managed by surface treatment such as microfracture, autologous chondrocyte transplantation, mosaicplasty, or periosteal grafting. An osteochondral defect less than 3 cm in diameter and less than 1 cm in depth can be managed by autologous chondrocyte transplantation, mosaicplasty or periosteal grafting. An osteochondral defect greater than 3 cm in diameter and 1 cm in depth is best managed by an osteochondral allograft. If there is an associated knee deformity, then an osteotomy should also be performed with all of the aforementioned procedures. In our series of osteochondral allografts for large post-traumatic knee defects realignment osteotomy is performed about 60% of the time in order to off load the transplant. To correct varus we realign the proximal tibia with an opening wedge osteotomy. To correct valgus, we realign the distal femur with a closing wedge osteotomy. Our results with osteochondral allografts for the large osteochondral defects of the knee have been excellent in 85% of patients at an average follow-up of 10 years. The Kaplan-Meier survivorship at 15 years is 72%. At an average follow-up of 22 years in 58 patients with distal femoral osteochondral allograft, 13 have been revised (22%). The 15-year survivorship was 84%. The results for the hip are early. To date we have performed this procedure on 16 patients. Surgical dislocation of the hip is carried out via a trochanteric osteotomy and the defect defined and trephined out. A press-fit fresh osteochondral allograft is inserted using the trephine technique. We have published our early results on a series of 8 patients with 5 good to excellent results, 1 fair result and 2 failures.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".