Vertebral Body Reshaping after Fractures: An Important Index of Recovery in Glucocorticoid-Treated Children
Bibliographic record
Abstract
PURPOSE: In this 6-year study we identified factors associated with spontaneous vertebral body reshaping in glucocorticoid (GC)-treated children with leukemia, rheumatic disorders, and nephrotic syndrome. METHODS: Subjects were 79 children (mean age 7.4 years) who had vertebral fracture (VF) evaluation on lateral spine radiographs at least 1 year after VF detection. VF were graded using the modified Genant semiquantitative method and fracture burden for individuals was quantified using the spinal deformity index (SDI; sum of grades from T4 to L4). RESULTS: Sixty-five children (82.3%) underwent complete vertebral body reshaping (median time from VF detection to complete reshaping 1.3 years by Cox proportional hazard modeling). Of 237 VF, the majority (83.1%) ultimately reshaped, with 87.2% reshaping in the thoracic region vs 70.7% in the lumbar region (P = .004). Cox models showed that (1) every g/m2 increase in GC exposure in the first year after VF detection was associated with a 19% decline in the probability of reshaping; (2) each unit increase in the SDI at the time of VF detection was associated with a 19% decline in the probability of reshaping [hazard ratio (HR) = 0.81; 95% confidence interval (CI) = 0.71, 0.92; P = .001]; (3) each additional VF present at the time of VF detection reduced reshaping by 25% (HR = 0.75; 95% CI = 0.62, 0.90; P = .002); and (4) each higher grade of VF severity decreased reshaping by 65% (HR = 0.35; 95% CI = 0.21, 0.57; P < .001). CONCLUSION: After experiencing a VF, children with higher GC exposure, higher SDI, more severe fractures, or lumbar VF were at increased risk for persistent vertebral deformity.
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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 | 0.002 |
| 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".