The efficacy of guided growth as an initial strategy for Blount disease treatment
Bibliographic record
Abstract
PURPOSE: The aim of this study was to evaluate the success of guided growth by temporal hemiepiphysiodesis of the lateral proximal tibia as a first line treatment option for Blount disease. METHODS: This was a retrospective multicentre study conducted in five centres, covering data on 55 limbs in 45 patients, with an average follow-up of 24.5 months following plate insertion. Preoperative alignment analysis was compared with three measurements taken postoperatively. The normalization of the mechanical medial proximal tibia angle (mMPTA) was defined as the primary outcome measure. RESULTS: Mean age at surgery was 9.5 years. Average preoperative mMPTA was 77°. On average, at 24.5 months post-surgery, mMPTA was 86.33°, while 43/55 limbs (78.18%) have achieved normalization (mMPTA 85° to 90°). Average rate of correction was 1° per month. When grouping the children as infantile (11 limbs), juvenile (12 limbs) and adolescent (32 limbs), operated on before the age of four years, between four and ten years and after the age of ten years, respectively, 63.63%, 66.67%, 87.5% have completed correction of deformity during the follow-up period. Interestingly, the femoral component of the deformity has achieved correction as well in 33/55 limbs (64%). CONCLUSION: Hemiepiphysiodesis is an effective first line treatment for Blount disease. Overall success rate is good but varies according to child's age. Adolescent Blount has the best chance of achieving full correction while same treatment is less effective in infantile Blount.Level of evidence: IV.
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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) | 0.000 | 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) | 0.001 | 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".