The Effect of Selective Dorsal Rhizotomy on Hip Displacement in Children With Cerebral Palsy
Bibliographic record
Abstract
BACKGROUND: Hip displacement is common in children with cerebral palsy (CP). Spasticity in the hip adductor muscles, hip flexors, and medial hamstrings has been identified as a possible cause of progressive hip displacement. Selective dorsal rhizotomy (SDR) aims to reduce lower extremity spasticity in children with CP. Here, we investigate the influence of SDR on hip displacement in children with CP at long-term follow-up, a minimum of 5 years post-SDR. METHODS: A retrospective review of children undergoing SDR at a Canadian pediatric hospital was completed. Migration percentage (MP) was measured on pelvis radiographs taken in the 6 months before SDR and minimum 5 years post-SDR or before hip surgery. The number of hips with displacement, defined as MP >30%, and the number of children with at least 1 hip displaced were determined. A linear mixed-effects model was used to assess potential risk factors for poor outcome post-SDR, defined as having MP >40% or surgical intervention for hip displacement. RESULTS: Ninety children [50 males, 40 females, Gross Motor Function Classification System (GMFCS) levels I to V: 1/13/24/43/9] with a mean follow-up of 8.5 years (SD 5.1) were included. The mean age at SDR was 4.9 years (SD 1.5); more than half of children (52%) had hip displacement at the time of SDR. Post-SDR, MP exceeded 30% in 0 (0%) of children at GMFCS level I, 1 (8%) at II, 11 (46%) at III, 31 (72%) at IV, and 7 (78%) at V. A poor outcome was associated with preoperative MP, age, and GMFCS level. CONCLUSIONS: The incidence of hip displacement post-SDR was consistent with population-based studies when evaluated by GMFCS. Our findings suggest that SDR has neither a positive nor negative effect on hip displacement when assessed at least 5 years postintervention. LEVEL OF EVIDENCE: Level 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 | 0.004 |
| 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".