INTRODUCTION: Chronic gastroduodenal disorders are highly prevalent and encompass heterogeneous pathophysiology. Autonomic dysfunction may contribute, but its relative contribution to disease pathophysiology and symptoms is unclear. This study aimed to clarify the contributions of autonomic dysfunction in gastroduodenal disorders and symptom associations using multimodal testing, hypothesizing that distinct disorder subgroups would be revealed. METHODS: This prospective observational cohort study included 80 patients with chronic gastroduodenal symptoms. Participants underwent expert clinician-led autonomic evaluation, body surface gastric mapping (BSGM; Gastric Alimetry, New Zealand), and gastric emptying scintigraphy testing. BSGM phenotyping, gastric emptying cutoffs, and autonomic criteria followed standard criteria. RESULTS: Gastroparesis was diagnosed in 27 (33.8%) patients. Autonomic dysfunction was present in 21 (26.3%) patients, and abnormal BSGM in 20 (25%). Autonomic dysfunction and abnormal BSGM were both more prevalent in gastroparesis (51.9% vs 13.2%, P = 0.001; and 44.4% vs 15.1%, P = 0.005, respectively). However, these diagnoses tended not to overlap, with patients having autonomic dysfunction generally exhibiting normal BSGM tests. Autonomic dysfunction independently predicted delayed gastric emptying (OR 13.8, P < 0.001) and was associated with earlier meal responses (higher meal response ratio, P = 0.04) and worse postprandial distress symptoms ( P < 0.05). DISCUSSION: Autonomic dysfunction independently predicted delayed gastric emptying and revealed a gastroparesis mechanism distinct from gastric myoelectrical impairment. Autonomic dysfunction altered the postprandial gastric motor response and was associated with a greater symptom burden. Multimodal physiological testing including autonomic profiling reveals distinct mechanistic subgroups in patients with chronic gastroduodenal symptoms.
Learning from complaints about surgical care: a large language model-assisted sequential methods analysis
Background Patient complaints provide a complementary lens on surgical safety, yet prior analyses have been small and specialty specific. We aimed to characterise themes, perioperative processes, system factors and outcomes in surgical complaints using a scalable, large language model (LLM)-assisted approach. Methods We performed a national, retrospective, cross-sectional, sequential methods study of publicly available investigation reports from the Aotearoa New Zealand Health and Disability Commissioner. All reports published since 1998 were retrieved and screened for relevance across all surgical specialties. A fixed-parameter LLM workflow extracted demographics, clinical context, complications, breached patient rights, system factors and outcomes. An inductive LLM-supported thematic synthesis generated complaint themes, followed by rescoring of theme relevance across reports. Human validation supported the LLM-assisted process (Cohen’s κ >0.85). Results Of 1827 reports screened, 650 involved surgical care. Postoperative complications were frequent (84.2%), commonly accompanied by permanent disability (33.2%) or death (24.2%). Delays in recognition of deterioration (76.1%), escalation (58.3%) and definitive management (49.5%) of complications were common and associated with higher mortality. Prominent themes concerned postoperative clinical management/monitoring, communication/informed consent and professional conduct/competence. Technical/procedural errors and medication errors were comparatively less common. Breaches are most often related to reasonable care and skill, and to informed consent, with marked variation across specialties. Conclusions Complaints about surgical care predominantly reflect postoperative monitoring, escalation and communication rather than intraoperative technical errors. System priorities should include robust deterioration recognition and response, reliable handover and escalation pathways, and strengthened consent and communication processes. An LLM-assisted pipeline can scale complaint analysis to support organisational learning and quality improvement.