The Accuracy of Incident Vertebral Fracture Detection in Children Using Targeted Case-Finding Approaches
Bibliographic record
Abstract
ABSTRACT Vertebral fractures are clinically important sequelae of a wide array of pediatric diseases. In this study, we examined the accuracy of case-finding strategies for detecting incident vertebral fractures (IVF) over 2 years in glucocorticoid-treated children (n = 343) with leukemia, rheumatic disorders, or nephrotic syndrome. Two clinical situations were addressed: the prevalent vertebral fracture (PVF) scenario (when baseline PVF status was known), which assessed the utility of PVF and low lumbar spine bone mineral density (LS BMD; Z-score <−1.4), and the non-PVF scenario (when PVF status was unknown), which evaluated low LS BMD and back pain. LS BMD was measured by dual-energy X-ray absorptiometry, vertebral fractures were quantified on spine radiographs using the modified Genant semiquantitative method, and back pain was assessed by patient report. Forty-four patients (12.8%) had IVF. In the PVF scenario, both low LS BMD and PVF were significant predictors of IVF. Using PVF to determine which patients should have radiographs, 11% would undergo radiography (95% confidence interval [CI] 8–15) with 46% of IVF (95% CI 30–61) detected. Sensitivity would be higher with a strategy of PVF or low LS BMD at baseline (73%; 95% CI 57–85) but would require radiographs in 37% of children (95% CI 32–42). In the non-PVF scenario, the strategy of low LS BMD and back pain produced the highest specificity of any non-PVF model at 87% (95% CI 83–91), the greatest overall accuracy at 82% (95% CI 78–86), and the lowest radiography rate at 17% (95% CI 14–22). Low LS BMD or back pain in the non-PVF scenario produced the highest sensitivity at 82% (95% CI 67–92), but required radiographs in 65% (95% CI 60–70). These results provide guidance for targeting spine radiography in children at risk for IVF. © 2021 American Society for Bone and Mineral Research (ASBMR).
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".