Zhicheng Fang

Background: Hyponatremia associated with the syndrome of inappropriate antidiuretic hormone secretion (SIADH) induced by chronic alcoholism exhibits high treatment resistance. The mechanisms underlying traditional treatment failure are often overlooked in clinical practice. Case Presentation: We report a case of a 59-year-old male patient with a 30-year history of heavy alcohol consumption, presenting with persistent serum sodium levels below 130 mmol/L for 3 months (nadir 116 mmol/L). Despite 3 months of aggressive sodium supplementation and fluid restriction therapy, serum sodium fluctuated between 116 and 125 mmol/L. Laboratory investigations revealed plasma ADH of 42 pg/mL (normal <5 pg/mL), plasma osmolality of 242 mOsm/kg, and urine osmolality of 486 mOsm/kg, consistent with SIADH diagnosis. Key Observations: Traditional treatment failure demonstrated a distinctive pattern: paradoxical increase in urine output during fluid restriction (intake 800 mL vs. urine output 1,200–1800 mL); paradoxical decrease in serum sodium following sodium supplementation (130→116 mmol/L) with compensatory increase in urinary sodium excretion (60→100 mmol/L); spironolactone increased urinary sodium excretion but serum sodium continued to decline. Intervention and Outcome: Treatment was switched to oral tolvaptan 15 mg once daily. Serum sodium increased to 128 mmol/L within 48 h and normalized to 135–138 mmol/L by day 7, with a correction rate of 8–9 mmol/L/24 h (within safe limits). No osmotic demyelination syndrome occurred. At 3-month follow-up, serum sodium remained stable within normal range. Novelty: This report provides the first systematic analysis of the specific resistance mechanisms of alcohol-induced SIADH to traditional treatment, revealing the molecular mechanisms underlying the “sodium paradox” (sodium supplementation paradoxically worsening hyponatremia), including V2 receptor upregulation leading to excessive water reabsorption, impaired hepatic ADH inactivation, and excessive compensatory renal sodium excretion. Conclusion: Tolvaptan demonstrates rapid, effective, and controllable therapeutic effects in alcohol-related refractory hyponatremia. This case emphasizes the importance of ADH measurement and pathophysiological mechanism analysis in such patients, providing a foundation for individualized treatment decisions.

Background: Acute methanol intoxication is life-threatening and typically presents with severe metabolic acidosis and central nervous system injury. Intracranial hemorrhage associated with methanol poisoning is uncommon and may be clinically occult, potentially delaying diagnosis and intervention. Case: We report a case of high-dose oral methanol poisoning presenting with profound metabolic acidosis and coma. Emergent cranial CT revealed basal ganglia hemorrhage. The patient underwent intermittent hemodialysis and received supportive pharmacotherapy. Owing to obstructive hydrocephalus and neurological deterioration, frontal burr-hole drilling and ventricular drainage were performed. Although metabolic derangements partially improved, the patient remained comatose after 1 week of hospitalization. The family requested transfer to another hospital for further management; however, the patient died during inter-hospital transport. Conclusion: Cerebral hemorrhage secondary to methanol poisoning can be insidious and may occur within hours to days after ingestion. Early neuroimaging and timely intervention are crucial when neurological deterioration or hydrocephalus is suspected.

Large language models (LLMs) have shown potential in medical text generation. Senior physician ward round records are critical documents whose quality reflects the accuracy and continuity of clinical decision-making. The initial record is particularly important, as it represents the first formal senior-level synthesis of a patient's presentation, establishing the diagnostic framework and treatment direction for all subsequent care. The quality of LLM-generated initial records for acute poisoning remains unclear. Focusing on patients with acute poisoning, this study systematically compared medical record writing quality among DeepSeek, ChatGPT (OpenAI), and human physicians to clarify the clinical value of LLMs. A retrospective analysis included 256 cases of acute poisoning from the emergency department ward of Taihe Hospital, Hubei University of Medicine. DeepSeek-V3.2-Exp and GPT-5.1 generated senior physician ward round records from standardized Chinese-language prompts, which were compared with the original medical charts. Blinded evaluations were performed by 3 senior emergency physicians, who scored overall quality across 5 dimensions on a Likert scale (from 1 to 5): case characteristics, current diagnosis, differential diagnosis, treatment plan, and prognosis assessment. Error frequencies were documented under 3 categories (inaccuracies, omissions, and fabrications), and potential harm was assessed using a modified Agency for Healthcare Research and Quality harm scale. DeepSeek achieved the highest mean total score (24.14, SD 0.90), which was significantly higher than ChatGPT (23.30, SD 1.42; P<.001) and the physician group (23.86, SD 0.86; P=.02). DeepSeek had the highest score for differential diagnosis (mean 4.98, SD 0.10) and prognosis assessment (mean 4.73, SD 0.42) and was comparable to physicians in case characteristics (DeepSeek: mean 4.90, SD 0.23; physicians: mean 4.96, SD 0.15; P>.001). For drug and pesticide poisoning, DeepSeek's mean total scores (24.23, SD 0.75 and 23.92, SD 1.14, respectively) were significantly higher than ChatGPT's (23.34, SD 1.33 and 22.78, SD 1.33, respectively; P<.001 for both). In biological toxin poisoning, DeepSeek (mean 23.97, SD 0.96) and physicians (mean 24.26, SD 0.62) scored similarly, both significantly higher than ChatGPT (mean 22.53, SD 1.86; P<.001). Overall potential harm scores were low across all 3 groups (<1 point), without significant differences (P=.38), although high-harm records were significantly more frequent in both LLM groups than in the physician group (P=.02). LLMs performed satisfactorily in generating initial senior physician ward round records for acute poisoning, with DeepSeek particularly outperforming the physician group in differential diagnosis and prognosis assessment and showing potential to assist clinical documentation. However, the significantly higher proportion of high-harm errors in LLM-generated records underscores the need for mandatory physician review before incorporation into official medical records.

BACKGROUND Large language models (LLMs) have demonstrated potential in certain medical text generation tasks. Senior physician ward round records are critical medical documents in the diagnostic and treatment process, with their quality reflecting the accuracy and continuity of clinical decision-making. The quality of senior physician rounds notes generated by large language models (LLMs) in the specific context of acute poisoning remains unclear. OBJECTIVE This study focused on acute poisoning patients and systematically compared the differences in medical record writing quality among the domestic model DeepSeek, the international model ChatGPT, and human physicians across multiple dimensions to clarify their clinical application value. METHODS A retrospective analysis was conducted on 256 poisoning cases from the emergency department ward of Taihe Hospital, Hubei University of Medicine. The study utilized DeepSeek V3.2 Exp and ChatGPT-5.1 to generate senior physician ward round records based on standardized prompts, which were then compared with actual records from the original medical charts. Three senior emergency physicians conducted blinded evaluations, scoring overall quality across five dimensions using a Likert 1-5 scale: case characteristics, current diagnosis, differential diagnosis, treatment plan, and prognosis assessment. Error frequencies in medical records were also documented. Additionally, potential harm was assessed using a modified AHRQ harm scale. RESULTS The DeepSeek model achieved the highest mean total score (24.14 ± 0.90), significantly higher than ChatGPT (23.30±1.42, P< 0.001) and the physician group (23.86±0.86, P=0.015). DeepSeek demonstrated optimal performance in both differential diagnosis (4.98±0.10) and prognosis assessment (4.73±0.42), while matching the physician group's performance (4.96± 0.15) in case characteristics (4.90±0.23) (P>0.05). Subgroup analysis showed that for drug poisoning and pesticide poisoning, DeepSeek's mean total scores (24.23±0.75, 23.92 ±1.14) were significantly higher than ChatGPT (23.34±1.33, 22.78 ± 1.33; both P<0.001). In biological toxin poisoning, DeepSeek (23.97±0.96) and the physician group (24.26±0.62) had similar scores, both significantly higher than ChatGPT (22.53±1.86, P<0.001). All between-group differences were statistically significant (P<0.05). The overall potential harm scores for errors were low across all three groups (<1 point), with no statistically significant differences (P = 0.38). CONCLUSIONS LLMs demonstrated satisfactory performance in generating initial senior physician ward round records for acute poisoning cases, particularly outperforming the physician group in case characteristics integration and treatment plan formulation, showing potential for assisting clinical documentation.LLMs can serve as effective tools to help physicians reduce documentation workload, but physician review and oversight remain essential. CLINICALTRIAL none

Objective: To investigate the clinical and MRI features of severe central nervous system injury caused by an extremely high dose of metoclopramide in combination with multiple drug overdoses, and to summarize treatment strategies and prognosis.Methods: We report the clinical course of acute poisoning in a 36-year-old woman following a single oral intake of approximately 350 mg metoclopramide combined with adrenolizone and coenzyme Q10. Clinical manifestations, laboratory findings, imaging results (CT and MRI), and treatment interventions after admission were collected and analyzed. The possible pathological mechanisms were explored in conjunction with previous literature.Results: Upon admission, the patient exhibited acute mental and behavioral disturbances, extrapyramidal symptoms, and impaired consciousness. Initial CT scans revealed no abnormalities. On the fourth day after admission, MRI demonstrated symmetrical patchy hyperintensities on T2WI/T2-FLAIR in the corpus callosum, bilateral corona radiata, and centrum semiovale white matter, along with hyperintensities on DWI and corresponding low ADC values, indicating cytotoxic edema consistent with toxic-metabolic encephalopathy. Following comprehensive multidisciplinary management-including early gastrointestinal decontamination, activated charcoal adsorption, HA380 hemoperfusion, hyperbaric oxygen therapy, neuroprotection, and symptomatic support-the patient was discharged on day 12. At the 3-month follow-up, no neurological sequelae were observed.Conclusion: Extremely high-dose metoclopramide, particularly in combination with other drugs, can cause a characteristic symmetrical white matter injury pattern of toxic encephalopathy, with MRI findings offering high diagnostic value. Early recognition, prompt gastrointestinal decontamination, blood purification, and multi-target neuroprotective therapy can markedly improve prognosis.

Objective: This study aims to assess the prognosis of patients with multiple organ dysfunction syndrome (MODS) caused by wasp stings by analyzing early systemic immune inflammation index (SII) and systemic inflammation response index (SIRI) values. Methods: A retrospective analysis was conducted on 151 patients admitted to the Department of Emergency Medicine, Taihe Hospital, Hubei University of Medicine, from January 2019 to December 2023. Among them, 60 patients developed MODS and 91 patients did not (non-MODS group). Early peripheral blood cell count-derived inflammation indices such as white blood cell count, platelet count, absolute neutrophil value, absolute lymphocyte value, absolute monocyte value, NLR, SII, and SIRI, along with baseline demographics, were compared between the two groups. Logistic regression analyses were performed, and the predictive accuracy of SII and SIRI was evaluated using receiver operating characteristic (ROC) curves, with the area under the curve (AUC) used to quantify sensitivity. Results: Significant differences were observed between the non-MODS and MODS groups in terms of age, hospitalization duration, number of stings, APACHE II score, time to first medical visit, SII, and SIRI (P < 0.05). Logistic regression analysis confirmed that both SII and SIRI were significant predictors of prognosis (P < 0.05). The AUC for SII and SIRI were 0.776 and 0.853, respectively, indicating their predictive value. Conclusion: Elevations in SII and SIRI are associated with the severity of wasp sting injuries, and early SII and SIRI values can serve as prognostic indicators for predicting MODS development and mortality in wasp sting patients.

Background: Wasp stings are a significant public health concern in many parts of the world, particularly in tropical and subtropical regions. The venom of wasps contains a variety of bioactive compounds that can lead to a wide range of clinical effects, from mild localized pain and swelling to severe, life-threatening allergic reactions, such as anaphylaxis. With the rapid development of artificial intelligence (AI) technologies, large language models (LLMs) are increasingly being used in health care, including emergency medicine and toxicology. These models have the potential to assist health care professionals in making fast and informed clinical decisions. This study aimed to assess the performance of 4 leading LLMs—ERNIE Bot 3.5 (Baidu), ERNIE Bot 4.0 (Baidu), Claude Pro (Anthropic), and ChatGPT 4.0—in managing wasp sting cases, with a focus on their accuracy, comprehensiveness, and decision-making abilities. Objective: The objective of this research was to systematically evaluate and compare the capabilities of the 4 LLMs in the context of wasp sting management. This involved analyzing their responses to a series of standardized questions and real-world clinical scenarios. The study aimed to determine which LLMs provided the most accurate, complete, and clinically relevant information for the management of wasp stings. Methods: This study used a cross-sectional design, creating 50 standardized questions that covered 10 key domains in the management of wasp stings, along with 20 real-world clinical case scenarios. Responses from the 4 LLMs were independently evaluated by 8 domain experts, who rated them on a 5-point Likert scale based on accuracy, completeness, and usefulness in clinical decision-making. Statistical comparisons between the models were made using the Wilcoxon signed-rank test, and the consistency of expert ratings was assessed using the Kendall coefficient of concordance. Results: Claude Pro achieved the highest average score of 4.7 (SD 0.603) out of 5, followed closely by ChatGPT 4.0 with a score of 4.5. ERNIE Bot 4.0 and ERNIE Bot 3.5 received average scores of 4 (SD 0.600) and 3.8, respectively. In analyzing the 20 complex clinical cases, Claude Pro significantly outperformed ERNIE Bot 3.5, particularly in areas such as managing complications and assessing the severity of reactions (P<.001). The expert ratings showed moderate agreement (Kendall W=0.67), indicating that the assessments were consistently reliable. Conclusions: The results of this study suggest that Claude Pro and ChatGPT 4.0 are highly capable of providing accurate and comprehensive support for the clinical management of wasp stings, particularly in complex decision-making scenarios. These findings support the increasing role of AI in emergency and toxicological medicine and suggest that the choice of AI tool should be based on the specific needs of the clinical situation, ensuring that the most appropriate model is selected for different health care applications.

Introduction: Severe acute poisoning from Compound paracetamol and Amantadine hydrochloride tablets can result in various complications, including acute liver injury, central nervous system reactions, and gastrointestinal symptoms.Case summary: A 15-year-old previously healthy female ingested 108 Compound paracetamol and Amantadine hydrochloride tablets. She presented to the emergency department with severe nausea, vomiting, abdominal pain, and distension, along with confusion. Emergency interventions, including gastric lavage and endotracheal intubation for respiratory support, were initiated. She was subsequently transferred to the intensive care unit for advanced life support, including nasogastric tube, hemoperfusion, and hemodialysis. During treatment, she developed coagulation abnormalities, liver injury, acute gastric mucosal bleeding, electrolyte imbalances, and psychiatric symptoms. A computed tomography (CT) scan revealed diffuse subcutaneous pneumatosis extending to the neck, anterior chest wall, thoracic cavity, and mediastinum. Following aggressive treatment, the patient was successfully discharged on the seventh day after the overdose.Conclusion: Acute liver injury and diffuse subcutaneous pneumatosis affecting multiple regions, caused by the ingestion of a large quantity of Compound paracetamol and Amantadine hydrochloride tablets, is a rare occurrence. With early gastric lavage, blood purification, and supportive therapy, the patient was successfully treated and discharged.