Magnetically Controlled Growing Rods Graduation
Bibliographic record
Abstract
STUDY DESIGN: A multicenter retrospective review of consecutive series of patients. OBJECTIVE: Long-term experience with using the magnetically controlled growing rods (MCGR) to treat patients with deformity in the growing spine to the conclusion of treatment with posterior spine fusion. SUMMARY OF BACKGROUND DATA: MCGR treatment for growing spine gained popularity with paucity of long-term follow up data. We hypothesized that final fusion might be more effective in bringing additional correction of the spine deformity after treatment with MCGR than that reported after traditional growing rods (TGR) due to less scarring and auto-fusion. METHODS: Retrospective review of 47 patients with varied etiology, treated between 2011 and 2017 which graduated treatment were followed in five academic medical centers for average of 50 months (range, 10-88). RESULTS: The initial mean coronal deformity of 69.6° (95% CI 65-74) was corrected to 40° (95% CI 36-40) immediately after the MCGR implantation but progressed to 52.8° (95% CI 46-59) prior to the final surgery (P < 0.01). Nevertheless, thoracic spine growth (T1-T12 height) improved from 187.3 mm (95% CI 179-195) following index surgery to 208.9 mm (95% CI 199-218) prior to final fusion (P < 0.01). Significant correction and spinal length were obtained at final fusion, but metallosis was a frequent observation (47%, 22/47). The average growth rate was 0.5 mm/month (95% CI 0.3-0.6). The overall complication rate within our cohort was 66% (31/47) with 45% (21/47) of unplanned returns to the operating theater. 32% (15/47) of the patients had an implant related complication. Unplanned surgery was highly correlated with thoracic kyphosis greater than 40° (OR 5.42 95% CI 1.3-23). CONCLUSION: Treatment of growing spine deformities with MCGR provides adequate control of spine deformity it is comparable to previously published data about TGR. The overall high complications rate over time and specifically implant related complications.Level of Evidence: 4.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".