Use of a Lateral Offset Short-Leg Walking Cast Before High Tibial Osteotomy
Bibliographic record
Abstract
The clinical results after high tibial osteotomy for the treatment of symptomatic varus gonarthrosis are unpredictable. Although preoperative gait analysis has been shown to be useful in predicting successful outcome after high tibial osteotomy, there are no readily available preoperative clinical tests for predicting success. The authors did a study to determine the effects of an offset short-leg walking cast as a potential predictor of clinical success after high tibial osteotomy. Specifically, the authors evaluated the effect of an offset short-leg walking cast on pain relief and changes in the peak external adduction moments in patients with symptomatic varus gonarthrosis indicated for high tibial osteotomy. Nineteen consecutive patients indicated for high tibial osteotomy were enrolled and completed the study. All patients had precast gait analysis to determine baseline parameters. Immediately after gait analysis, a short-leg lateral offset walking cast was applied and worn for 3 days to allow time for adaptation. Gait analysis then was repeated. Western Ontario and McMaster Universities Osteoarthritis Index pain scores were obtained before and after the cast was applied. The cast resulted in a mean reduction in pain of 53%, and a mean reduction in the peak external adduction moment of 36% in the 17 of 19 patients who tolerated the cast. The reduction in pain was correlated with the reduction in the peak adduction moment (r = 0.63). The authors conclude that an offset short-leg walking cast results in pain reduction that correlates with changes in external adduction moments about the knee. Therefore, an offset short-leg walking cast may prove to be an effective tool for predicting patients who ultimately will benefit from valgus high tibial osteotomy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".