Objective:To analyze the clinical efficacy and learning curve of robotic-assisted Warshaw procedure using the da Vinci system.Methods:Clinical data of 91 consecutive patients with pancreatic body and tail lesions undergoing robotic-assisted Warshaw procedure at Qingdao University Affiliated Hospital from October 2021 to April 2023 were retrospectively analyzed, including 21 males and 70 females, aged (50.2±14.3) years. Patient characteristics, operative time, and intraoperative blood loss were recorded. The learning curve was constructed using cumulative sum (CUSUM) analysis, with case number on the x-axis and CUSUM values on the y-axis. Linear fitting was performed, and the model with the highest determination coefficient was selected as the optimal fitting model. The learning process was divided into two phases based on the inflection point of the CUSUM learning curve: the learning phase and the proficiency phase. Perioperative outcomes were compared between these two phases.Results:All 91 procedures were successfully completed using the da Vinci robotic system with R0 resection margins. There were no perioperative mortalities or reoperations due to postoperative splenic infarction. The operative time was (227.84±76.68) min. The optimal fitting equation for the CUSUM learning curve was: CUSUM=0.005 640X 3-1.501X 2+ 92.59X-183.1. The CUSUM learning curve showed an inflection point at case 39, dividing the learning process into the learning phase (cases 1-39) and proficiency phase (cases 40-91). Compared to the learning phase, the proficiency phase demonstrated significantly shorter operative time [(203.0±75.6) min vs. (260.9±65.5) min], less intraoperative blood loss [50.0 (20.0, 50.0) ml vs. 100.0 (50.0, 100.0) ml], and reduced postoperative drainage duration [(8.7±2.4) d vs. (10.8±3.2) d] (all P<0.05). Conclusion:The robotic-assisted Warshaw procedure feasible for patients with pancreatic body and tail lesions. Surgeons require approximately 39 cases to complete the learning curve and achieve proficiency in this procedure.

Objectives:To explore the efficacy of robotic-assisted retroperitoneal benign tumor resection and to analyze its learning curve.Methods:This is a retrospective case series study. The data of patients who underwent robotic-assisted retroperitoneal benign tumor resection from August 2015 to February 2023 at the Department of Retroperitoneal Tumor Surgery was analyzed retrospectively. There were 24 males and 45 females, with an age of (46.3±10.6) years (range: 19 to 76 years). The perioperative data, postoperative pathological results, and follow-up data were recorded. The cumulative sum (CUSUM) method was used to analyze the robotic system setup time and operative time to plot the learning curve. A linear regression model was applied to determine the best-fit curve, selecting the model with the highest R2 value. Based on the vertex of the learning curve for surgical time, the patients were divided into a learning group and a mastery group. The general data and perioperative conditions of the two groups were compared. Independent sample t-tests, Mann-Whitney U tests, and χ2 tests were used for comparisons. Results:All 69 patients successfully completed the surgery without intraoperative complications. The diameter of tumors was (49.7±18.6) mm (range: 16 to 131 mm). The setup time for the robotic surgical system was (35.3±9.8) minutes (range: 20 to 61 minutes); the surgical time was (169.2±36.5) minutes (range: 70 to 305 minutes); intraoperative blood loss ( M(IQR)) was 10.0 (15.0) ml (range: 2.0 to 200.0 ml). The tumors in 32 patients (46.4%) were adherent to major blood vessels. All patients were discharged without complications. The follow-up period lasted until February 2024, and no patients required reoperation, readmission, or died due to retroperitoneal benign tumors. There were no severe long-term complications, and no radiological evidence of tumor recurrence was found. The best-fit equation for the learning curve based on surgical time was CUSUM=0.010X3-1.648X2-68.573X-61.091, and the best-fit equation for the learning curve based on robotic system setup time was CUSUM=0.0018X3-0.285X2+10.460X+57.541 (where X represents the number of surgeries). The R2 values of 2 learning curve models were 0.953 and 0.957, respectively, and the fit model tests had P<0.05. The inflection point of the learning curve based on surgical time was the 28th case, which is considered the minimum number of surgeries required to achieve proficiency in robotic-assisted retroperitoneal benign tumor resection. Based on this, the patients were divided into a learning group (cases 1 to 28) and a mastery group (cases 29 to 69). The surgical time for the learning group was significantly longer than that of the mastery group ((194.7±30.0) minutes vs. (151.9±31.4) minutes, t=4.126, P<0.01). No statistically significant differences were found for other parameters (all P>0.05). Conclusions:Robotic-assisted retroperitoneal benign tumor resection is feasible. The minimum number of surgeries required to achieve proficiency in overcoming the learning curve is about 28 cases.

ObjectiveTo elucidate the critical role of rehabilitation medical records (including electronic records) in rehabilitation medicine's clinical practice and management, comprehensively analyzed the structure, core content and data standards of rehabilitation medical records, to develop a standardized medical record data architecture and core dataset suitable for rehabilitation medicine and to explore the application of rehabilitation data in performance evaluation and payment. MethodsBased on the regulatory documents Basic Specifications for Medical Record Writing and Basic Specifications for Electronic Medical Records (Trial) issued by National Health Commission of China, and referencing the World Health Organization (WHO) Family of International Classifications (WHO-FICs) classifications, International Classification of Diseases (ICD-10/ICD-11), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI Beta-3), this study constructed the data architecture, core content and data standards for rehabilitation medical records. Furthermore, it explored the application of rehabilitation record summary sheets (home page) data in rehabilitation medical statistics and payment methods, including Diagnosis-related Groups (DRG), Diagnosis-Intervention Packet (DIP) and Case Mix Index. ResultsThis study proposed a systematic standard framework for rehabilitation medical records, covering key components such as patient demographics, rehabilitation diagnosis, functional assessment, rehabilitation treatment prescriptions, progress evaluations and discharge summaries. The research analyzed the systematic application methods and data standards of ICD-10/ICD-11, ICF and ICHI Beta-3 in the fields of medical record terminology, coding and assessment. Constructing a standardized data structure and data standards for rehabilitation medical records can significantly improve the quality of data reporting based on the medical record summary sheet, thereby enhancing the quality control of rehabilitation services, effectively supporting the optimization of rehabilitation medical insurance payment mechanisms, and contributing to the establishment of rehabilitation medical performance evaluation and payment based on DRG and DIP. ConclusionStructured rehabilitation records and data standardization are crucial tools for quality control in rehabilitation. Systematically applying the three reference classifications of the WHO-FICs, and aligning with national medical record and electronic health record specifications, facilitate the development of a standardized rehabilitation record architecture and core dataset. Standardizing rehabilitation care pathways based on the ICF methodology, and developing ICF- and ICD-11-based rehabilitation assessment tools, auxiliary diagnostic and therapeutic systems, and supporting terminology and coding systems, can effectively enhance the quality of rehabilitation records and enable interoperability and sharing of rehabilitation data with other medical data, ultimately improving the quality and safety of rehabilitation services.

ObjectiveTo explore the data standard system of electronic medical records in the field of rehabilitation, focusing on the terminology and coding standards, data structure, and key content categories of rehabilitation electronic medical records. MethodsBased on the Administrative Norms for the Application of Electronic Medical Records issued by the National Health Commission of China, the electronic medical record standard architecture issued by the International Organization for Standardization and Health Level Seven (HL7), the framework of the World Health Organization Family of International Classifications (WHO-FICs), Basic Architecture and Data Standards of Electronic Medical Records, Basic Data Set of Electronic Medical Records, and Specifications for Sharing Documents of Electronic Medical Records, the study constructed and organized the data structure, content, and data standards of rehabilitation electronic medical records. ResultsThe data structure of rehabilitation electronic medical records should strictly follow the structure of electronic medical records, including four levels (clinical document, document section, data set and data element) and four major content areas (basic information, diagnostic information, intervention information and cost information). Rehabilitation electronic medical records further integrated information related to rehabilitation needs and characteristics, emphasizing rehabilitation treatment, into clinical information. By fully applying the WHO-FICs reference classifications, rehabilitation electronic medical records could establish a standardized framework, diagnostic criteria, functional description tools, coding tools and terminology index tools for the coding, indexing, functional description, and analysis and interpretation of diseases and health problems. The study elaborated on the data structure and content categories of rehabilitation electronic medical records in four major categories, refined the granularity of reporting rehabilitation content in electronic medical records, and provided detailed data reporting guidance for rehabilitation electronic medical records. ConclusionThe standardization of rehabilitation electronic medical records is significant for improving the quality of rehabilitation medical services and promoting the rehabilitation process of patients. The development of rehabilitation electronic medical records must be based on the national and international standards. Under the general electronic medical records data structure and standards, a rehabilitation electronic medical records data system should be constructed which incorporates core data such as disease diagnosis, functional description and assessment, and rehabilitation interventions. The standardized rehabilitation electronic medical records scheme constructed in this study can support the improvement of standardization of rehabilitation electronic medical records data information.

ObjectiveTo analyze the data structure and standards of rehabilitation outpatient medical records, to provide data support for improving the quality of rehabilitation outpatient care and developing medical insurance payment policies. MethodsBased on the normative documents issued by the National Health Commission, Basic Standards for Medical Record Writing and Standards for Electronic Medical Record Sharing Documents, in accordance with the Quality Management Regulations for Outpatient (Emergency) Diagnosis and Treatment Information Pages (Trial), reference to the framework of the World Health Organization Family of International Classifications (WHO-FICs), the data framework and content of rehabilitation outpatient medical records were determined, and the data standards were discussed. ResultsThis study constructed a data framework for rehabilitation outpatient medical records, including four main components: patient basic information, visit process information, diagnosis and treatment information, and cost information. Three major reference classifications of WHO-FICs, International Classification of Diseases, International Classification of Functioning, Disability and Health, and International Classification of Health Interventions,were used to establish diagnostic standards and standardized terminology, as well as coding disease diagnosis, functional description, functional assessment, and rehabilitation interventions, to improve the quality of data reporting, and level of quality control in rehabilitation. ConclusionThe structuring and standardization of rehabilitation outpatient medical records are the foundation for sharing of rehabilitation data. The using of the three major classifications of WHO-FICs is valuable for the terminology and coding of disease diagnosis, functional description and assessment, and intervention in rehabilitation outpatient medical records, which is significant for sharing and interconnectivity of rehabilitation outpatient data, as well as for optimizing the quality and safety of rehabilitation medical services.

ObjectiveTo explore the standardization of inpatient rehabilitation medical record summary sheet, encompassing its structure, content and data standards, to enhance the standardization level of inpatient rehabilitation medical record summary sheet, improve data reporting quality, and provide accurate data support for medical insurance payment, hospital performance evaluation, and rehabilitation discipline evaluation. MethodsBased on the relevant specifications of the National Health Commission's Basic Norms for Medical Record Writing, Specifications for Sharing Documents of Electronic Medical Records, and Quality Management and Control Indicators for Inpatient Medical Record Summary Sheet (2016 Edition), this study analyzed the structure and content of the inpatient rehabilitation medical record summary sheet. The study systematically applied the three major reference classifications of the World Health Organization Family of International Classifications, International Classification of Diseases (ICD-10/ICD-11, ICD-9-CM-3), International Classification of Functioning, Disability and Health (ICF), and International Classification of Health Interventions (ICHI Beta-3), for disease diagnosis, functional description and assessment, and rehabilitation intervention, forming a standardized terminology system and coding methods. ResultsThe inpatient rehabilitation medical record summary sheet covered four major sections: inpatient information, hospitalization information, diagnosis and treatment information, and cost information. ICD-10/ICD-11 were the standards and coding tools for admission and discharge diagnoses in the inpatient rehabilitation medical record summary sheet. The three functional assessment tools recommended by ICD-11, the 36-item version of World Health Organization Disability Assessment Schedule 2.0, Brief Model Disability Survey and Generic Functioning domains, as well as ICF, were used for rehabilitation functioning assessment and the coding of outcomes. ICHI Beta-3 and ICD-9-CM-3 were used for coding surgical procedures and operations in the medical record summary sheet, and also for coding rehabilitation intervention items. ConclusionThe inpatient rehabilitation medical record summary sheet is a summary of the relevant content of the rehabilitation medical record and a tool for reporting inpatient rehabilitation data. It needs to be refined and optimized according to the characteristics of rehabilitation, with necessary data supplemented. The application of ICD-11/ICD-10, ICF and ICHI Beta-3/ICD-9-CM-3 classification standards would comprehensively promote the accuracy of inpatient diagnosis of diseases and functions. Based on ICD-11 and ICF, relevant functional assessment result data would be added, and ICHI Beta-3/ICD-9-CM-3 should be used to code rehabilitation interventions. Improving the quality of rehabilitation medical records and inpatient rehabilitation medical record summary sheet is an important part of rehabilitation quality control, and also lays an evidence-based data foundation for the analysis and application of inpatient rehabilitation medical record summary sheet.

Aim To study the difference in hepatotox-icity of psoralen on normal rats and Yin-deficiency rats from the perspective of lipid metabolism,so as to help explain the mechanism of psoralen cautiously used in patients with Yin deficiency recorded in ancient books.Methods SD rats were randomly divided into the nor-mal control group(carboxymethyl cellulose-Na,CMC-Na),normal administration group(CMC-Na+psor-alen),Yin-deficiency control group(CMC-Na+thy-roxine)and Yin-deficiency administration group(CMC-Na+thyroxine+psoralen).The model of Yin-deficiency was established by thyroxine(1 mg·kg-1)for ten days,and then psoralen(200 mg·kg-1)was given for three days.The serum indexes related to liver injury were detected by automatic biochemical analy-zer,the morphological changes of liver tissue were ob-served using HE and oil red O staining,and the relative transcription levels of lipid metabolism related enzymes and mRNA of transporter and endoplasmic reticulum stress related factors were detected using Real-time PCR.Results After intragastric administration of psoralen for three days,compared with the normal group,the levels of serum alanine aminotransferase(ALT),aspartate transaminase(AST),total bile acid(TBA)and triglyeride(TG)in Yin deficiency group increased more significantly,while TC,ALB and TP de-creased more significantly,and liver HE and oil red O staining showed more obvious lipid degeneration.TG synthesis factors adrenocortical carcinoma(ACC),fatty acid synthase(FASN)and sterolregulatory element binding protein-1(SREBP-1)were down-regulated more significantly,TG transport factors mili-total pro-tein(MTP)and lipoprotein pipase(LPL)were down-regulated more evidently,fatty acid β-oxidation related factors carnitine palmitoyltransferase 1A(CPT1A),carnitine/organic cation transporter 2(OCTN2)and peroxisome proliferators-activated receptors-alpha(PPARα)were down-regulated more apparently,TC transporter adenosine triphosphate binding cassette transporter G8(ABCG8)and bile acid receptor farne-soid X receptor(FXR)were down-regulated more ob-viously,and endoplasmic reticulum stress factor activa-ting transcription factor 4(ATF4)was up-regulated more significantly.Conclusions Psoralen can cause more severe hepatotoxicity in Yin deficiency rats than that in normal administration group,and its mechanism may be related to the disorder of hepatic lipid metabo-lism,aggravation of hepatic cholestasis and steatosis,and activation of endoplasmic reticulum stress re-sponse.

Aim To study the difference in hepatotox-icity of psoralen on normal rats and Yin-deficiency rats from the perspective of lipid metabolism,so as to help explain the mechanism of psoralen cautiously used in patients with Yin deficiency recorded in ancient books.Methods SD rats were randomly divided into the nor-mal control group(carboxymethyl cellulose-Na,CMC-Na),normal administration group(CMC-Na+psor-alen),Yin-deficiency control group(CMC-Na+thy-roxine)and Yin-deficiency administration group(CMC-Na+thyroxine+psoralen).The model of Yin-deficiency was established by thyroxine(1 mg·kg-1)for ten days,and then psoralen(200 mg·kg-1)was given for three days.The serum indexes related to liver injury were detected by automatic biochemical analy-zer,the morphological changes of liver tissue were ob-served using HE and oil red O staining,and the relative transcription levels of lipid metabolism related enzymes and mRNA of transporter and endoplasmic reticulum stress related factors were detected using Real-time PCR.Results After intragastric administration of psoralen for three days,compared with the normal group,the levels of serum alanine aminotransferase(ALT),aspartate transaminase(AST),total bile acid(TBA)and triglyeride(TG)in Yin deficiency group increased more significantly,while TC,ALB and TP de-creased more significantly,and liver HE and oil red O staining showed more obvious lipid degeneration.TG synthesis factors adrenocortical carcinoma(ACC),fatty acid synthase(FASN)and sterolregulatory element binding protein-1(SREBP-1)were down-regulated more significantly,TG transport factors mili-total pro-tein(MTP)and lipoprotein pipase(LPL)were down-regulated more evidently,fatty acid β-oxidation related factors carnitine palmitoyltransferase 1A(CPT1A),carnitine/organic cation transporter 2(OCTN2)and peroxisome proliferators-activated receptors-alpha(PPARα)were down-regulated more apparently,TC transporter adenosine triphosphate binding cassette transporter G8(ABCG8)and bile acid receptor farne-soid X receptor(FXR)were down-regulated more ob-viously,and endoplasmic reticulum stress factor activa-ting transcription factor 4(ATF4)was up-regulated more significantly.Conclusions Psoralen can cause more severe hepatotoxicity in Yin deficiency rats than that in normal administration group,and its mechanism may be related to the disorder of hepatic lipid metabo-lism,aggravation of hepatic cholestasis and steatosis,and activation of endoplasmic reticulum stress re-sponse.

Objective:To analyze the clinical efficacy and learning curve of robotic-assisted Warshaw procedure using the da Vinci system.Methods:Clinical data of 91 consecutive patients with pancreatic body and tail lesions undergoing robotic-assisted Warshaw procedure at Qingdao University Affiliated Hospital from October 2021 to April 2023 were retrospectively analyzed, including 21 males and 70 females, aged (50.2±14.3) years. Patient characteristics, operative time, and intraoperative blood loss were recorded. The learning curve was constructed using cumulative sum (CUSUM) analysis, with case number on the x-axis and CUSUM values on the y-axis. Linear fitting was performed, and the model with the highest determination coefficient was selected as the optimal fitting model. The learning process was divided into two phases based on the inflection point of the CUSUM learning curve: the learning phase and the proficiency phase. Perioperative outcomes were compared between these two phases.Results:All 91 procedures were successfully completed using the da Vinci robotic system with R0 resection margins. There were no perioperative mortalities or reoperations due to postoperative splenic infarction. The operative time was (227.84±76.68) min. The optimal fitting equation for the CUSUM learning curve was: CUSUM=0.005 640X 3-1.501X 2+ 92.59X-183.1. The CUSUM learning curve showed an inflection point at case 39, dividing the learning process into the learning phase (cases 1-39) and proficiency phase (cases 40-91). Compared to the learning phase, the proficiency phase demonstrated significantly shorter operative time [(203.0±75.6) min vs. (260.9±65.5) min], less intraoperative blood loss [50.0 (20.0, 50.0) ml vs. 100.0 (50.0, 100.0) ml], and reduced postoperative drainage duration [(8.7±2.4) d vs. (10.8±3.2) d] (all P<0.05). Conclusion:The robotic-assisted Warshaw procedure feasible for patients with pancreatic body and tail lesions. Surgeons require approximately 39 cases to complete the learning curve and achieve proficiency in this procedure.

Objectives:To explore the efficacy of robotic-assisted retroperitoneal benign tumor resection and to analyze its learning curve.Methods:This is a retrospective case series study. The data of patients who underwent robotic-assisted retroperitoneal benign tumor resection from August 2015 to February 2023 at the Department of Retroperitoneal Tumor Surgery was analyzed retrospectively. There were 24 males and 45 females, with an age of (46.3±10.6) years (range: 19 to 76 years). The perioperative data, postoperative pathological results, and follow-up data were recorded. The cumulative sum (CUSUM) method was used to analyze the robotic system setup time and operative time to plot the learning curve. A linear regression model was applied to determine the best-fit curve, selecting the model with the highest R2 value. Based on the vertex of the learning curve for surgical time, the patients were divided into a learning group and a mastery group. The general data and perioperative conditions of the two groups were compared. Independent sample t-tests, Mann-Whitney U tests, and χ2 tests were used for comparisons. Results:All 69 patients successfully completed the surgery without intraoperative complications. The diameter of tumors was (49.7±18.6) mm (range: 16 to 131 mm). The setup time for the robotic surgical system was (35.3±9.8) minutes (range: 20 to 61 minutes); the surgical time was (169.2±36.5) minutes (range: 70 to 305 minutes); intraoperative blood loss ( M(IQR)) was 10.0 (15.0) ml (range: 2.0 to 200.0 ml). The tumors in 32 patients (46.4%) were adherent to major blood vessels. All patients were discharged without complications. The follow-up period lasted until February 2024, and no patients required reoperation, readmission, or died due to retroperitoneal benign tumors. There were no severe long-term complications, and no radiological evidence of tumor recurrence was found. The best-fit equation for the learning curve based on surgical time was CUSUM=0.010X3-1.648X2-68.573X-61.091, and the best-fit equation for the learning curve based on robotic system setup time was CUSUM=0.0018X3-0.285X2+10.460X+57.541 (where X represents the number of surgeries). The R2 values of 2 learning curve models were 0.953 and 0.957, respectively, and the fit model tests had P<0.05. The inflection point of the learning curve based on surgical time was the 28th case, which is considered the minimum number of surgeries required to achieve proficiency in robotic-assisted retroperitoneal benign tumor resection. Based on this, the patients were divided into a learning group (cases 1 to 28) and a mastery group (cases 29 to 69). The surgical time for the learning group was significantly longer than that of the mastery group ((194.7±30.0) minutes vs. (151.9±31.4) minutes, t=4.126, P<0.01). No statistically significant differences were found for other parameters (all P>0.05). Conclusions:Robotic-assisted retroperitoneal benign tumor resection is feasible. The minimum number of surgeries required to achieve proficiency in overcoming the learning curve is about 28 cases.