High tibial osteotomy with use of the Taylor Spatial Frame external fixator for osteoarthritis of the knee.
Bibliographic record
Abstract
BACKGROUND: High tibial osteotomy (HTO) is used to treat medial compartment osteoarthritis of the knee in active patients with varus alignment. In this study we review the clinical and radiographic outcomes associated with the Taylor Spatial Frame (Smith & Nephew), and its use in HTOs, and we include an illustrative case report. METHODS: In 7 patients with medial compartment osteoarthritis of the knee and varus alignment, the Taylor Spatial Frame was applied to the tibia in the operating room and a proximal tibial osteotomy was performed. Patients followed a computer-generated turning schedule until the desired correction was achieved. The frame was removed when the osteotomy site had healed. The lower extremity measure (LEM) was used to assess physical function. Clinical outcome measures relating to the Taylor Spatial Frame included latency, time to correction, time in the frame, number of residual corrections and complications. Radiographic outcomes included preoperative Resnick grades of osteoarthritis, pre- and post-correction limb alignment and tibial slope measurements. RESULTS: Average (and standard deviation) LEM grade at a mean 41 (14) months follow-up after correction was 94% (5%). Average latency was 8 days, time to correction was 15 days, time in the frame was 23 weeks and number of residual corrections was 1.3. Complications were similar to those for external fixators. Radiographic correction goals were met in all patients. CONCLUSION: The Taylor Spatial Frame is a valuable asset when using HTO to treat medial compartment osteoarthritis of the knee.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".