Traditional Growth-Friendly Implants Result in Improved Health-Related Quality of Life in Cerebral Palsy Patients with Early-Onset Scoliosis
Bibliographic record
Abstract
Background/Objectives: In an effort to promote personalized medicine, the purpose was to (1) analyze health-related quality of life (HRQoL) in cerebral palsy (CP) patients treated with growth-friendly implants for early-onset scoliosis (EOS), and (2) compare traditional implants (traditional growing rods [TGRs], VEPTR) with magnetically controlled growing rods (MCGRs). Methods: Twenty-four patients with CP and EOS were identified from an international multicenter database. Mean EOSQ-24 domain and total scores and absolute differences from pre-index surgery to the minimum two-year follow-up were compared. Results: For all patients: Pre-index surgery EOSQ-24 total score: 48.9 vs. follow-up: 53.8. Follow-up scores were greater than at pre-op for 10 of the 12 domains, with the only significant difference being activities of daily living. Growth-friendly implants had positive absolute differences for 8 of the 12 domains and in the total score. Nine traditional implant patients had a pre-index surgery EOSQ-24 total of 45.8 points, while 15 MCGRs patients had a score of 50.8 points. At follow-up, traditional implant patients had greater scores than at pre-index surgery for all 12 domains, with total score of 55.1 points, and positive absolute differences for all domains (non-significant). MCGRs had greater scores than at pre-index surgery for six domains, with a total score of 53.1 points (non-significant), and positive absolute differences for seven domains. Traditional implants had a significantly greater absolute difference for emotion than MCGRs (p = 0.030). Conclusions: At the minimum two-year follow-up, CP patients had small, but statistically non-significant, improvements in HRQoL following growth-friendly surgery. Compared to MCGRs, traditional implants provided a modest additional benefit in HRQoL.
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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) | 0.000 | 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".