Comparing the Effects of Two Feeding Methods on Metabolic Bone Disease in Newborns with Very Low Birth Weights
Bibliographic record
Abstract
INTRODUCTION: Bone metabolic disease is an important issue in newborns with very low birth weight. The 80 percent of the transport of calcium (Ca) and phosphor (P) from mother to fetus takes place in the third trimester of pregnancy. This transport process is impaired with the preterm delivery of the newborn. On the other side, breast milk and formula are not competent resources to supply sufficient amounts of Ca and P to meet the requirements of the preterm newborn, thereby a greater reduction in the storage of these minerals. The current study has been done with the purpose of comparing the effects of two feeding methods on the indices of metabolic bone disease in newborns with very low birth weights (VLBW). MATERIALS & METHODS: The study design was cohort and the study was done on a total of 58 newborns with very low birth weights in Kashan Shahid Beheshti Hospital. The newborns were divided into two groups with 29 placed in the group of alternate feeding on preterm formula (preNan) and the other 29 placed in the group of breast milk and preterm formula (preNan). Eventually, the indices of bone metabolic disease were measured in both groups and were statistically analyzed. RESULTS: The difference between the mean serum levels of Ca in different weeks and also between the two groups were significant (p=0.001). However, the changes in mean serum level of P in the two groups were not significant (P=0.151). The comparison of serum levels of alkaline phosphatase between the two groups indicated that their difference was significant and that they had been influenced by the feeding method (P=0.001). The serum level of bicarbonate, when compared between the two groups, was found to make a significant difference (P=0.001). The difference between the two feeding methods in precipitating rickets and osteopenia was not significant. CONCLUSION: According to the findings of current study, feeding on preterm formula (preNan) is associated with better and more desirable results, though the occurrence of rickets in the two groups had no significant difference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".