Normative data for whole body, femur and lumbar spine bone mineral content in healthy term infants from birth to 1 year of age
Bibliographic record
Abstract
The use of bone mineral densitometry is increasingly used to evaluate bone disease in pediatrics; however, interpretation is difficult due to a lack of appropriate reference data. A prospective study was conducted to assess bone mineral content (BMC) in infants born at term (37 to 42 weeks gestation), weight appropriate for gestational age, and free of congenital malformations. The group consisted of 33 boys and 26 girls recruited from the Winnipeg Health Sciences Center (Manitoba, Canada). Whole‐body (WB), lumbar spine (vertebrae 1–4) and femur BMC were measured using dual‐energy x‐ray absorptiometry (DXA; QDR 4500A, Hologic Inc., Waltham, MA) in array mode. All measurements of BMC increased linearly over the study period (Table 1). WB BMC adjusted for weight increased (p<0.01) by 12% during the year. After adjusting for weight, both spine and femur decreased (p<0.01) by 30% and 15%, respectively from baseline to 6 months. Relative to 6 months, by 12 months BMC of spine increased (p<0.01) by 16% and femur (p<0.01) were no longer different from baseline values. There were no differences in BMC between boys and girls after adjustment for total weight. This data represents the accretion of BMC during the first year of life and will aid in the interpretation of diagnostic DXA scans in pediatrics.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".