Visceral Pain in Preterm Infants with Necrotizing Enterocolitis: Underlying Mechanisms and Implications for Treatment
Bibliographic record
Abstract
Necrotizing enterocolitis (NEC) is a relatively rare but very severe gastrointestinal disease primarily affecting very preterm infants. NEC is characterized by excessive inflammation and ischemia in the intestines, and is associated with prolonged, severe visceral pain. Despite its recognition as a highly painful disease, current pain management for NEC is often inadequate, and research on optimal analgesic therapy for these patients is lacking. Insight into the mechanisms underlying intestinal pain in infants with NEC-visceral pain-could help identify the most effective analgesics for these vulnerable patients. Therefore, this comprehensive review aims to provide an overview of visceral nociception, including transduction, transmission, modulation, and experience, and discuss the implications for analgesic therapy in preterm infants with NEC. The transmission of visceral pain differs from that of somatic pain, contributing to the diffuse nature of visceral pain. Studies evaluating the effectiveness of analgesics for treating visceral pain in infants are scarce. However, research in visceral pain models highlights agents that may be particularly effective for treating visceral pain based on their mechanisms of action. Further research is necessary to determine whether agents that have shown promise for treating visceral pain in preclinical studies and adults are effective in infants with NEC as well.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".