Rotational Guided Growth: A Preliminary Study of Its Use in Children
Bibliographic record
Abstract
Torsional malalignment of the legs is common in children, and those that do not remodel may benefit from surgical correction. Traditionally, this is corrected with an open osteotomy. Guided growth is the gold standard for minimally invasive angular correction and has been investigated for use in torsional deformities. This study presents our preliminary results of rotationally guided growth in the femur and tibia using a novel technique of peripheral flexible tethers. A total of 8 bones in 5 patients were treated with flexible tethers consisting of separated halves of a hinge plate (Orthopediatrics Pega Medical, Montreal, QC, Canada), which were fixed to the epiphysis and metaphysis at 45° angles to the physis and connected with Fibertape (Arthrex, Naples, FL, USA). The implants are placed medially and laterally in the opposite 45° inclination, determined by the desired direction of rotation. Additionally, the average treatment time was 12 months. All patients corrected the rotational malalignment by clinical evaluation. The average rotational change was 30° in the femurs and 9.5° in the tibias. Further, the average follow-up was 18 months, with no recurrence of the rotational deformity. There was no change in longitudinal growth in the patients who underwent bilateral treatment. Rotational guided growth with flexible tether devices is a novel technique that successfully corrects torsional malalignment without invasive osteotomy surgery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".