Abnormal gastric electrophysiology following laparoscopic sleeve gastrectomy and associations with symptoms and quality of life
Bibliographic record
Abstract
Abstract Background Sleeve gastrectomy is an effective bariatric procedure, however may lead to persistent symptoms without obvious mechanical cause. The normal gastric pacemaker region, which lies on the greater curvature of the corpus, is resected in sleeve gastrectomy, however, the electrophysiological consequences are not adequately defined. This study assessed these impacts and associations with symptoms and quality of life (QoL), using non-invasive gastric mapping. Methods Patients with previous sleeve gastrectomy underwent body surface gastric mapping (Gastric Alimetry, New Zealand), comprising 30-minute fasting baseline and 4-hr post-prandial recordings. Analysis encompassed Principal Gastric Frequency (PGF), BMI-adjusted amplitude, Gastric Alimetry Rhythm Index (GA-RI), with comparison to reference intervals and matched controls. Symptoms were evaluated using a validated App and questionnaires. Results 38 patients (median 36 months post-surgery; range 6-119 months) and 38 controls were recruited. 35/38 patients had at least one abnormal parameter, typically reduced frequencies (2.3±0.34 vs controls 3.08±0.21; p<0.001) and amplitudes (14.8±6.9 vs 31.5±17.8; p<0.001). Patients exhibited higher symptoms and lower QoL (PAGI-SYM 20 vs controls 7, p<0.001; PAGI-QOL 27 vs 136, p<0.001). Gastric amplitude and GA-RI correlated positively with bloating (r=0.71, p<0.001 and r=0.60, p=0.02) while amplitude correlated negatively with heartburn (r=-0.46, p=0.03). Lower gastric amplitudes also correlated with greater weight loss (r=-0.45; p=0.014). Conclusion Sleeve gastrectomy modifies gastric electrophysiology due to pacemaker resection, with variable remodelling. Substantial reductions in gastric frequency and amplitude occur routinely after surgery, and specific relationships between post-sleeve gastric amplitude, symptoms of heartburn and bloating, and weight loss are identified.
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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) | 0.000 | 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".