Bibliographic record
Abstract
Preterm infants are particularly susceptible to abnormal colonization and are therefore prone to systemic infections due to increased intestinal permeability to potentially pathogens. Abnormal pattern of colonization in pre-term infants may contribute to the pathogenesis of neonatal Necrotizing Enterocolitis (NEC), an acquired gastrointestinal (GI) disease associated with significant morbidity and mortality. Introduction of foods containing probiotic cultures may be advantageous as probiotics prevented gut colonization by abnormal flora. Endeavour has been made to explore the mechanism of gut colonization, suitability of breast milk for preterm infants, effect of administration of probiotics to preterm infants and its safety concerns. Human milk is also suitable for the management of premature infants but fortified breast milk may be a preferred choice and not the pooled pasteurized breast milk. Based upon Randomized Controlled Trials administration of probiotic in preterm infants with a birth weight >1000 g could be recommended due to significantly reduction in incidence of NEC and no systemic infections or serious adverse events was reported. Administration of probiotics in preterm neonates is recommended but further research is emerging for its routine application. Probiotic supplementation in preterm neonates exhibited good safety profile and did not show any side effects and can be recommended for preterm infants but not for extremely low birth weight (ELBW) infants [1, 2].
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".