The Choice of Normative Pediatric Reference Database Changes Spine Bone Mineral Density Z-Scores But Not the Relationship Between Bone Mineral Density and Prevalent Vertebral Fractures
Bibliographic record
Abstract
OBJECTIVES: Our objectives were to assess the magnitude of the disparity in lumbar spine bone mineral density (LSBMD) Z-scores generated by different reference databases and to evaluate whether the relationship between LSBMD Z-scores and vertebral fractures (VF) varies by choice of database. PATIENTS AND DESIGN: Children with leukemia underwent LSBMD by cross-calibrated dual-energy x-ray absorptiometry, with Z-scores generated according to Hologic and Lunar databases. VF were assessed by the Genant method on spine radiographs. Logistic regression was used to assess the association between fractures and LSBMD Z-scores. Net reclassification improvement and area under the receiver operating characteristic curve were calculated to assess the predictive accuracy of LSBMD Z-scores for VF. RESULTS: For the 186 children from 0 to 18 years of age, 6 different age ranges were studied. The Z-scores generated for the 0 to 18 group were highly correlated (r ≥ 0.90), but the proportion of children with LSBMD Z-scores ≤-2.0 among those with VF varied substantially (from 38-66%). Odds ratios (OR) for the association between LSBMD Z-score and VF were similar regardless of database (OR = 1.92, 95% confidence interval 1.44, 2.56 to OR = 2.70, 95% confidence interval 1.70, 4.28). Area under the receiver operating characteristic curve and net reclassification improvement ranged from 0.71 to 0.75 and -0.15 to 0.07, respectively. CONCLUSIONS: Although the use of a LSBMD Z-score threshold as part of the definition of osteoporosis in a child with VF does not appear valid, the study of relationships between BMD and VF is valid regardless of the BMD database that is used.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".