Growth modulation in idiopathic angular knee deformities: Is it predictable?
Bibliographic record
Abstract
PURPOSE: To evaluate the temporal and spatial sequence of events following temporal hemiepiphysiodesis in idiopathic knee varus/valgus. METHODS: This is a retrospective multicentre study on 372 physes in 206 patients. The average rate of correction (ROC) was calculated; univariate and multivariate analysis were performed. RESULTS: In all, 92% of the femoral physes were followed for more than one year/reached skeletal maturity. Of those, 93% were corrected to a mechanical lateral distal femoral angle (mLDFA) of 85° to 89°; 2% did not, while 5% were over-corrected. A total of 92% of the tibial physes were followed for more than one year/reached skeletal maturity. Of those, 92% were corrected to a mechanical medial proximal tibial angle (mMPTA) of 85° to 89°; 2% did not, while 6% were over-corrected. Factors significantly influencing success and ROC were age, direction and magnitude of deformity. Femoral ROC was significantly faster than tibial ROC: 0.85° versus 0.78°/month, respectively (p = 0.05). Femoral valgus ROC was significantly faster than varus ROC: 0.90° versus 0.77°/month, respectively (p = 0.04). A constant was derived to calculate the amount of correction. Significant correlation was found between calculated and actual mLDFA in valgus deformity during the first year (r = 0.58 to 0.87, p < 0.01). Calculated mLDFA of varus deformity did not correlate with actual mLDFA. Significant correlation was found when calculating mMPTA correction in all deformities. CONCLUSIONS: Femur corrects faster than tibia; valgus femoral deformities are corrected faster than varus. Valgus correction in the distal femur/proximal tibia as well as varus correction in the tibia in idiopathic patients is highly predictable. The constant derived is the first tool which enables predicting and monitoring amount of correction in hemiepiphysiodesis when correcting angular deformities around the knee. 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".