High tibial osteotomy to neutral alignment improves medial knee articular cartilage composition
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to: (1) test the hypothesis that HTO improves articular cartilage composition in the medial compartment without adversely affecting the lateral compartment and patella, and; (2) explore associations between knee alignment and cartilage composition after surgery. METHODS: 3T MRI and standing radiographs were obtained from 34 patients before and 1-year after HTO. Articular cartilage was segmented from T2 maps. Mechanical axis angle (MAA), posterior tibial slope, and patellar height were measured from radiographs. Changes in T2 and radiographic measures were assessed using paired t tests, and associations were assessed using Pearson correlation coefficients. RESULTS: The mean (SD) MAA before and after HTO was - 6.5° (2.4) and 0.6° (3.0), respectively. There was statistically significant shortening [mean (95%CI)] of T2 in the medial femur [- 2.8 ms (- 4.2; - 1.3), p < 0.001] and medial tibia [- 2.2 ms (- 3.3; - 1.0), p < 0.001], without changes in the lateral femur [- 0.5 ms (- 1.6; 0.6), p = 0.3], lateral tibia [0.2 ms (- 0.8; 1.1), p = NS], or patella [0.5 ms (- 1.0; 2.1), p = NS). Associations between radiographic measures and T2 were low. 23% of the increase in lateral femur T2 was explained by postoperative posterior tibial slope (r = 0.48). CONCLUSION: Performing medial opening wedge HTO without overcorrection improves articular cartilage composition in the medial compartment of the knee without compromising the lateral compartment or the patella. Although further research is required, these results suggest HTO is a disease structure-modifying treatment for knee OA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".