Quantifying the ‘law of diminishing returns’ in magnetically controlled growing rods
Bibliographic record
Abstract
Aims Magnetically controlled growing rods (MCGRs) allow non-invasive correction of the spinal deformity in the treatment of early-onset scoliosis. Conventional growing rod systems (CGRS) need repeated surgical distractions: these are associated with the effect of the ‘law of diminishing returns’. The primary aim of this study was to quantify this effect in MCGRs over sequential distractions. Patients and Methods A total of 35 patients with a maximum follow-up of 57 months were included in the study. There were 17 boys and 18 girls with a mean age of 7.4 years (2 to 14). True Distraction (TD) was determined by measuring the expansion gap on fluoroscopy. This was compared with Intended Distraction (ID) and expressed as the ‘T/I’ ratio. The T/I ratio and the Cobb angle were calculated at several time points during follow-up. Results The mean follow-up was 30 months (6 to 57). There was a significant decrease in the mean T/I ratio over time (convex rod at 3 months 0.81, sd 0.58 vs 51 months 0.17, sd 0.16, p = 0.0001; concave rod at 3 months 0.93, sd 0.67 vs 51 months 0.18, sd 0.15, p = 0.0001). A linear decline of the mean T/I ratios was noted for both convex rods (r2 = 0.90, p = 0.004) and concave rods (r2 = 0.81, p = 0.015) over 51 months. At the 24-month follow-up stage, there was a significant negative correlation between the mean T/I ratio of the concave rod with weight (r = -0.59, p = 0.01), age (r = -0.59, p = 0.01), and BMI of the child (r = -0.54, p = 0.01). Conclusions The ‘law of diminishing returns’ is also seen after serial distraction using MCGR. Compared to previously published data for CGRS, there is a gradual linear decline rather than a rapid initial decline in lengthening. In older, heavier children a reduced distraction ratio in the concave rod of the MCGR device is noted over time. Cite this article: Bone Joint J 2017;99-B:1658–64.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".