Invited Mini Review Metabolic Bone Disease of Prematurity: Overview and Practice Recommendations
Bibliographic record
Abstract
Metabolic bone disease of prematurity (MBDP) is defined by undermineralization of the preterm infant skeleton arising from inadequate prenatal and postnatal calcium (Ca) and phosphate (PO4) accretion. Severe MBDP can be associated with rickets and fractures. Despite advances in neonatal nutrition, MBDP remains prevalent in premature infants due to inadequate mineral accretion ex utero. There also remain significant knowledge gaps regarding best practices for monitoring and treatment of MBDP among neonatologists and pediatric endocrinologists. Preventing and treating MBDP can prevent serious consequences including rickets or pathologic fractures. Postnatal monitoring to facilitate early recognition of MBDP is best done by first-tier laboratory screening by measuring serum Ca, phosphorus, and alkaline phosphatase to identify infants at risk. If these laboratories are abnormal, further studies including assessing parathyroid hormone and/or tubular resorption of PO4 can help differentiate between Ca and PO4 deficiency as primary etiologies to guide appropriate treatment with mineral supplements. Additional research into optimal mineral supplementation for the prevention and treatment of MBDP is needed to improve long-term bone health outcomes and provide a fuller evidence base for future treatment guidelines. Metabolic bone disease of prematurity (MBDP) is defined by undermineralization of the preterm infant skeleton arising from inadequate prenatal and postnatal calcium (Ca) and phosphate (PO4) accretion. Severe MBDP can be associated with rickets and fractures. Despite advances in neonatal nutrition, MBDP remains prevalent in premature infants due to inadequate mineral accretion ex utero. There also remain significant knowledge gaps regarding best practices for monitoring and treatment of MBDP among neonatologists and pediatric endocrinologists. Preventing and treating MBDP can prevent serious consequences including rickets or pathologic fractures. Postnatal monitoring to facilitate early recognition of MBDP is best done by first-tier laboratory screening by measuring serum Ca, phosphorus, and alkaline phosphatase to identify infants at risk. If these laboratories are abnormal, further studies including assessing parathyroid hormone and/or tubular resorption of PO4 can help differentiate between Ca and PO4 deficiency as primary etiologies to guide appropriate treatment with mineral supplements. Additional research into optimal mineral supplementation for the prevention and treatment of MBDP is needed to improve long-term bone health outcomes and provide a fuller evidence base for future treatment guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".