Predicting Symptomatic Response to Prokinetic Treatment Using Gastric Alimetry
Bibliographic record
Abstract
BACKGROUND: Chronic neurogastroduodenal disorders are challenging to manage, with therapy often initiated on a trial and error basis. Prokinetics play a significant role in management, but responses are variable and have been associated with adverse events, impacting widespread use. We investigated whether body surface gastric mapping (BSGM) biomarkers (using Gastric Alimetry) could inform patient selection for prokinetic therapy. METHODS: Patients with chronic gastroduodenal symptoms taking oral prokinetic agents, regardless of gastric emptying status, were prospectively recruited and underwent BSGM (30 m baseline, 482 kcal standardized meal, 4 h postprandial recording) while off-prokinetic agents. Patients were followed up with daily symptom diaries. A subset was compared to matched patients not taking prokinetic agents. Prokinetic responders were defined based on symptom improvement greater than a minimum clinically important difference methodology. KEY RESULTS: Forty-two patients (88% female; median age 36; median BMI 26) taking prokinetics were analyzed. Prokinetic prescribing, compared to matched patients, was independent of BSGM metrics (p > 0.15). In patients on existing prokinetics (withheld for BSGM), lower amplitudes predicted reduced symptom burden, whereas low rhythm stability predicted a worse symptom burden (p < 0.05). In prokinetic-naive patients (i.e., started on a prokinetic during the study), a lower postprandial amplitude predicted responders (mean 37.5 ± 10.6 uV in responders [n = 5] vs. mean 54.8 ± 6.6 uV among nonresponders [n = 3], p = 0.047). CONCLUSIONS: Gastric Alimetry biomarkers may help in the prediction of prokinetic response in patients with chronic gastroduodenal symptoms. Lower postprandial amplitudes, indicating a reduced meal response, appear to predict benefit, while impaired rhythm stability predicted poorer therapeutic response.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".