Interventional nutritional protocol decreases osteopenia of prematurity in extremely low birth weight infants
Bibliographic record
Abstract
Background: Osteopenia of prematurity is common among extremely low birth weight infants (ELBW). There are currently no standard practices regarding screening, prevention or treatment of this condition. Objective: To determine if introduction of a nutritional monitoring and interventional protocol would decrease the incidence and severity of osteopenia of prematurity. Methods: A nutritional protocol to monitor the needs and provide supplementation of calcium and phosphorus has been instituted in our unit. We compared ELBW infants born in the year before (Group 1) vs. after (Group 2) for lowest serum phosphorus, peak alkaline phosphatase and bone fractures. Logistic regression analysis was used to determine the independent effect of gestational age, birth weight, diuretics, postnatal steroids, and the nutritional protocol. Results: Osteopenia-related outcomes improved, including: phosphorus level <3 mg/dL (34% vs. 14%, (P = 0.003)), peak alkaline phosphatase >750 IU/L (18% vs. 7%, (P = 0.018)), and bone fractures (16.4% vs. 5.4%, (P = 0.026)). The use of diuretics increased significantly, while the use of postnatal steroids decreased significantly. Logistic regression analysis confirmed the independent contribution of our nutritional protocol as well as birth weight to osteopenia of prematurity outcomes. Conclusions: This is the first study to report that initiation of a protocol for monitoring and optimizing bone mineralization can decrease the incidence of severe osteopenia of prematurity as manifested by hypophosphatemia, elevated ALP and bone fractures. Implementation of a neonatal intensive care clinical practice guideline will improve this largely preventable medical complication.
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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 | 0.003 |
| 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".