Effects of metoclopramide on gastric motility measured by short-term bio-impedance
Bibliographic record
Abstract
AIM: To analyze the accuracy of short-term bio-impedance as a means of measuring gastric motility. METHODS: We evaluated differences in the short-term electrical bio-impedance signal from the gastric region in the following conditions: (1) fasting state, (2) after the administration of metoclopramide (a drug that induces an increase in gastric motility) and (3) after food ingestion in 23 healthy volunteers. We recorded the real component of the electrical impedance signal from the gastric region for 1000 s. We performed a Fast Fourier Transform (FFT) on this data and then compared the signal among the fasting, medicated, and postprandial conditions using the median of the area under the curve, the relative area under the curve and the main peak activity. RESULTS: The median of the area under the curve of the frequency range in the region between 2-8 cycles per minute (cpm) decreased from 4.7 cpm in the fasting condition to 4.0 cpm in the medicated state (t = 3.32, P = 0.004). This concurred with the decrease seen in the relative area under the FFT curve in the region from 4 to 8 cpm from 38.3% to 26.6% (t = 2.81, P = 0.012) and the increase in area in the region from 2 to 4 cpm from 22.4% to 27.7%, respectively (t = -2.5, P = 0.022). Finally the main peak position also decreased in the region from 2 to 8 cpm. Main peak activity in the fasting state was 4.72 cpm and declined to 3.45 cpm in the medicated state (t = 2.47, P = 0.025). There was a decrease from the fasting state to the postprandial state at 3.02 cpm (t = 4.0, P = 0.0013). CONCLUSION: Short-term electrical bio-impedance can assess gastric motility changes in individuals experiencing gastric stress by analyzing the area medians and relative areas under the FFT curve.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".