Arguments against routine administration of probiotics for NEC prevention
Bibliographic record
Abstract
PURPOSE OF REVIEW: Necrotizing enterocolitis (NEC) is a leading cause of morbidity and mortality among premature neonates. Although randomized trials have shown that probiotics may be efficacious in the prevention of NEC, their use has not been universally adopted in the neonatal intensive care unit (NICU). Caveats regarding routine probiotic supplementation for the prevention of NEC are summarized in this review. RECENT FINDINGS: Accumulating evidence indicates that prophylactic probiotic supplementation in preterm infants can reduce the incidence of NEC. However, substantial knowledge gaps, regulatory issues, and implementation challenges should be addressed before probiotics are introduced as standard of care for all preterm neonates. Limitations of published trial data have made it challenging to define regimens that optimize efficacy and safety in specific patient subgroups. Moreover, the current probiotic market lacks rigorous regulatory oversight, which could raise concerns about the quality and safety of probiotic products. Finally, implementation pitfalls include risks of cross-colonization and resource requirements to monitor and mitigate potential adverse events. SUMMARY: Probiotics have shown promise in the prevention of NEC. However, there is insufficient evidence to guide the selection of optimal regimens. Furthermore, issues related to regulatory and institutional oversight should be addressed before supplementation is routinely implemented in NICUs.
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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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".