Metoclopramide does not increase gastric muscle contractility in newborn rats
Bibliographic record
Abstract
Feeding intolerance resulting from delayed gastric emptying is common in premature neonates. Metoclopramide (MCP), the most frequently used prokinetic drug in neonates, enhances gastric muscle contractility through inhibition of dopamine receptors. Although its therapeutic benefit is established in adults, limited data are available to support its clinical use in infants. Hypothesizing that developmentally dependent differences are present, we comparatively evaluated the effect of MCP on fundus muscle contractility in newborn, juvenile, and adult rats. The muscle strips were either contracted with electrical field stimulation (EFS) to induce cholinergic nerve-mediated acetylcholine release or carbachol, a cholinergic agonist acting directly on the muscarinic receptor. Although in adult rats MCP increased EFS-induced contraction by 294 ± 122% of control (P < 0.01), no significant effect was observed in newborn fundic muscle. MCP had no effect on the magnitude of the carbachol-induced and/or bethanechol-induced gastric muscle contraction at any age. In response to dopamine, an 80.7 ± 5.3% relaxation of adult fundic muscle was observed, compared with only a 8.4 ± 8.7% response in newborn tissue (P < 0.01). Dopamine D2 receptor expression was scant in neonates and significantly increased in adult gastric tissue (P < 0.01). In conclusion, the lack of MCP effect on the newborn fundic muscle contraction potential relates to developmental differences in dopamine D2 receptor expression. To the extent that these novel data can be extrapolated to neonates, the therapeutic value of MCP as a prokinetic agent early in life requires further evaluation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".