Hasil untuk "Diseases of the endocrine glands. Clinical endocrinology"

Rosa Lauretta, Andrea Sansone, Massimiliano Sansone et al.

Min Jae Kang, Min Jae Kang, Roopa Kanakatti Shankar et al.

PurposeSkeletal abnormalities are common in Turner Syndrome (TS), yet data on objective radiographic markers are limited. We aimed to establish normative reference ranges for phalangeal length ratios and assess their utility in detecting skeletal abnormalities in TS.MethodsWe analyzed 4,082 female bone age X-rays (<18 years) from the Radiological Society of North America (RSNA) database after quality screening and outlier exclusion as a reference cohort. Phalangeal length ratios—4th to 3rd metacarpal (4:3 MC), 5th to 3rd metacarpal (5:3 MC), and 5th to 3rd middle phalanx (5:3 MP)—were measured and compared in 81 TS patients seen at a single center. Additional skeletal features such as SHOX deficiency-related signs and brachydactyly type A3 (BDA3) were assessed.ResultsIn reference subjects, 4:3 MC and 5:3 MC ratios remained stable across most age groups, while the 5:3 MP ratio increased with age. TS patients showed a significantly lower 4:3 MC and 5:3 MP ratios (P < 0.001, P = 0.002, respectively) compared to ones from reference subjects. A low 4:3 MC ratio (<–2 SD) was seen in 27.2% of TS patients. The 4:3 MC ratio correlated with height percentile (r = 0.27, P = 0.02). BDA3 was more prevalent in TS compared to reference subjects (13.6% vs. 2.1%, P < 0.001) and associated with low MC ratios.ConclusionNormative reference ranges for phalangeal length ratios were established and differences in 4:3 and 5:3 MP ratios in patients with TS were identified compared to the reference group. Further studies with larger TS cohorts are needed to confirm the clinical utility of these radiographic biomarkers.

Francisco José Gárate, Paloma Chausa, Diego Moreno et al.

Empiric antibiotic prescribing in high-risk clinical contexts often requires decision making under conditions of incomplete information, where inappropriate coverage or unjustified escalation may compromise safety and antimicrobial stewardship. While clinical decision-support systems have been proposed to assist in this process, many approaches lack explicit governance and evaluation mechanisms defining scope, abstention conditions, recommendation permissibility, and expected system behavior. This work specifies a governance and evaluation framework for deterministic clinical decision-support systems operating under explicitly constrained scope. Deterministic behavior is adopted to ensure that identical inputs yield identical outputs, supporting transparency, auditability, and conservative decision support in high-risk prescribing contexts. The framework treats governance as a first-class design component, separating clinical decision logic from rule-based mechanisms that determine whether a recommendation may be issued. Explicit abstention, deterministic stewardship constraints, and exclusion rules are formalized as core constructs. The framework defines an evaluation methodology utilizing a fixed set of synthetic, mechanism-driven clinical cases with predefined expected behavior. This validation process focuses on behavioral alignment with specified rules rather than clinical effectiveness, predictive accuracy, or outcome optimization. Within this protocol, abstention is treated as a correct and intended outcome when governance conditions are not satisfied. The proposed framework provides a reproducible approach for specifying, governing, and inspecting deterministic clinical decision-support systems in empiric antibiotic prescribing contexts where transparency, auditability, and conservative behavior are prioritized.

Qobilov A.E.

Thyroid and parathyroid pathologies frequently manifest as complex, comorbid clinical conditions; however, the precise pathogenetic intersection between autoimmune thyroiditis (AIT) and secondary hyperparathyroidism remains insufficiently characterised in current literature. This observational study assessed the structural and functional interrelationship between these two endocrine entities in a cohort of 112 patients. Biochemical marker analysis — encompassing thyroid-stimulating hormone (TSH), parathyroid hormone (PTH), and ionised calcium — in conjunction with high-resolution ultrasonography, revealed profound disturbances in calcium-phosphorus metabolism initiated by primary hypothyroidism. Patients with uncompensated AIT demonstrated a statistically significant PTH elevation averaging 66.7% above baseline control values, which correlated directly with reactive parathyroid gland hyperplasia (r = 0.55; p < 0.05). Conversely, the thyrotoxic phase of subacute thyroiditis was characterised by transient hypercalcaemia accompanied by concurrent physiological PTH suppression. Echographic assessment identified hyperplastic parathyroid changes in 22.6% of the hypothyroid cohort. These metabolic disturbances necessitate timely, targeted intervention — specifically optimised cholecalciferol and calcium supplementation — to prevent irreversible osteological complications, including diminished bone mineral density. Incorporating routine PTH and ionised calcium monitoring into the diagnostic protocol for patients presenting with TSH levels exceeding 10 mcIU/ml substantially enhances therapeutic outcomes. This study provides evidence for a critical algorithmic transition in clinical endocrinology, advancing from isolated thyroid management towards a comprehensive, multi-glandular metabolic rehabilitation strategy.

Stijn J M Niessen, Ellen N. Behrend, F. Fracassi et al.

Simple Summary To make progress in the field of hormonal diseases in companion animals, it helps when researchers, clinicians, and educators use the same language. Currently, there is no consensus on basic concepts such as what constitutes the correct definition of diseases affecting the adrenal glands, important hormone-producing glands situated next to the kidneys. This publication reports on the second cycle of a novel project called “Agreeing Language in Veterinary Endocrinology” (ALIVE) that brings experts and those interested in the field together to try and achieve consensus on such disease definitions. The cycle’s methods were adapted from previous ones to improve efficiency and were completed successfully, accomplishing a majority-based consensus. It also delivered agreement on diagnostic criteria for adrenal diseases in companion animals. It is hoped the work will improve education, diagnosis, and treatment in this field, ultimately leading to improvements in the quality of life of animals suffering from adrenal disease.

Tao Sun, Jun Liu

BackgroundThis study seeks to investigate the association between the triglyceride-glucose index (TyG), triglyceride glucose index to high-density lipoprotein cholesterol ratio (TyG/HDL-c), and the risk of diabetes in individuals with nonalcoholic fatty liver disease (NAFLD).MethodsThis retrospective study encompassed 457 NAFLD patients from The Central Hospital of Shaoyang, monitored over a three-year period. Missing data were addressed using multiple imputation, and the Synthetic Minority Over-sampling Technique (SMOTE) was employed to balance the dataset. Multicollinearity analysis was conducted to evaluate the collinearity among variables, while principal component analysis was utilized to examine the distribution of variables in both the original and balanced datasets. A multivariate logistic regression model was used to assess the association between TyG, TyG/HDL-c, and the risk of diabetes in NAFLD patients, adjusting for various covariates. Subgroup analysis was performed to identify differences across diverse populations, and restricted cubic splines (RCS) were used to explore potential non-linear relationships. The receiver operating characteristic (ROC) curve examined the diagnostic value of individual and combined indicators in assessing the risk of diabetes in NAFLD patients.ResultsUpon adjustment for all covariates, TyG was found to significantly elevate the risk of diabetes among patients with NAFLD (OR = 1.96, 95% CI: 1.67-2.30, P &lt; 0.001), with a notable non-linear relationship observed (threshold: 2.39, P-nonlinear = 0.002). Similarly, TyG/HDL-c significantly increased diabetes risk (OR = 1.90, 95% CI: 1.60-2.26, P &lt; 0.001), also demonstrating a distinct non-linear association (threshold: 2.20, P-nonlinear &lt; 0.001). Subgroup analyses revealed significant interactions between TyG and TyG/HDL-c across different gender subgroups (P for interaction &lt; 0.05). The ROC curve analysis indicated that the combined use of TyG and TyG/HDL-c provided superior diagnostic performance for assessing diabetes risk in NAFLD patients (Area Under the Curve [AUC]: 0.703, 95% CI: 0.665-0.740), compared to the use of TyG (AUC: 0.694, 95% CI: 0.656-0.732) or TyG/HDL-c (AUC: 0.693, 95% CI: 0.655-0.731) independently.ConclusionBoth TyG and TyG/HDL-c are significantly associated with an increased risk of diabetes in NAFLD patients, exhibiting non-linear relationships. Furthermore, these associations vary significantly across gender subgroups, their combined use enhances risk assessment, supporting their clinical utility in evaluating diabetes risk.

Jing Tian, Yan Dong, Zhongping Xu et al.

ObjectivesThe aim of this study was to analyze the association between TyG-BMI and 365-day mortality in critically ill patients with CHD.MethodsPatient data were extracted from the MIMIC-IV database. All patients were categorized into 3 groups based on TyG-BMI index: Low TyG-BMI index group, Medium TyG-BMI index group, and High TyG-BMI index group. Outcomes included primary and secondary outcomes, with the primary outcome being 365-day mortality and the secondary outcomes being hospital survival, intensive care unit (ICU) survival, and 28-day, 90-day, and 180-day mortality. The Kaplan-Meier survival curves were used to compare the outcomes of the three groups. The relationship between TyG-BMI index and 365-day mortality was assessed using multivariate Cox proportional risk regression models and restricted cubic spline curves (RCS).Results889 critically ill patients with CHD were analyzed. Among them, 600 (67.50%) were male patients with a mean age of 68.37 years and 289 (32.50%) were female patients with a mean age of 73.91 years. Patients with a medium TyG-BMI index had the best 365-day prognostic outcome and the highest survival rate compared with patients in the Low and High TyG-BMI index groups [201 (67.68%) vs. 166 (56.08%), 188 (63.51%); P=0.013]. After fully adjusted modeling analysis, the hazard ratio (HR) for 365-day mortality was found to be 0.71 (95% CI 0.54-0.93, P=0.012) for the Medium TyG-BMI index group. Meanwhile, RCS analysis showed an L-shaped relationship between TyG-BMI index and 365-day mortality.ConclusionsThe TyG-BMI index is significantly associated with 365-day mortality in patients with severe CHD.

Simon A. Lee, Anthony Wu, Jeffrey N. Chiang

We introduce Clinical ModernBERT, a transformer based encoder pretrained on large scale biomedical literature, clinical notes, and medical ontologies, incorporating PubMed abstracts, MIMIC IV clinical data, and medical codes with their textual descriptions. Building on ModernBERT the current state of the art natural language text encoder featuring architectural upgrades such as rotary positional embeddings (RoPE), Flash Attention, and extended context length up to 8,192 tokens our model adapts these innovations specifically for biomedical and clinical domains. Clinical ModernBERT excels at producing semantically rich representations tailored for long context tasks. We validate this both by analyzing its pretrained weights and through empirical evaluation on a comprehensive suite of clinical NLP benchmarks.

Robert Korom, Sarah Kiptinness, Najib Adan et al.

We evaluate the impact of large language model-based clinical decision support in live care. In partnership with Penda Health, a network of primary care clinics in Nairobi, Kenya, we studied AI Consult, a tool that serves as a safety net for clinicians by identifying potential documentation and clinical decision-making errors. AI Consult integrates into clinician workflows, activating only when needed and preserving clinician autonomy. We conducted a quality improvement study, comparing outcomes for 39,849 patient visits performed by clinicians with or without access to AI Consult across 15 clinics. Visits were rated by independent physicians to identify clinical errors. Clinicians with access to AI Consult made relatively fewer errors: 16% fewer diagnostic errors and 13% fewer treatment errors. In absolute terms, the introduction of AI Consult would avert diagnostic errors in 22,000 visits and treatment errors in 29,000 visits annually at Penda alone. In a survey of clinicians with AI Consult, all clinicians said that AI Consult improved the quality of care they delivered, with 75% saying the effect was "substantial". These results required a clinical workflow-aligned AI Consult implementation and active deployment to encourage clinician uptake. We hope this study demonstrates the potential for LLM-based clinical decision support tools to reduce errors in real-world settings and provides a practical framework for advancing responsible adoption.

Yijie Zhu, Shan E Ahmed Raza

Cancer grade is a critical clinical criterion that can be used to determine the degree of cancer malignancy. Revealing the condition of the glands, a precise gland segmentation can assist in a more effective cancer grade classification. In machine learning, binary classification information about glands (i.e., benign and malignant) can be utilized as a prompt for gland segmentation and cancer grade classification. By incorporating prior knowledge of the benign or malignant classification of the gland, the model can anticipate the likely appearance of the target, leading to better segmentation performance. We utilize Segment Anything Model to solve the segmentation task, by taking advantage of its prompt function and applying appropriate modifications to the model structure and training strategies. We improve the results from fine-tuned Segment Anything Model and produce SOTA results using this approach.

A. Buzdin

Diseases of the endocrine system represent a serious public health problem and frequently can be caused by genetic factors or their combinations with environmental and lifestyle factors. Assessing relevant genetic factors is important to estimate the risk of endocrine pathologies in an individual patient before their manifestation. Identification of genetic variations in proteins of the major histocompatibility complex is important in connection with the autoimmune nature of many endocrine pathologies, including type 1 diabetes. In this study, we investigated the relationship between human leukocyte antigen (HLA) genes and 13 endocrine disorders by using experimental whole-exome sequencing profiles obtained for 895 patients from the National Medical Research Center for Endocrinology, Moscow. In addition, the linkage disequilibrium of the identified alleles in the context of the respective diagnoses was assessed. We identified totally 45 statistically significant associations between HLA alleles and specific diagnoses of endocrine pathologies. Among them, 33 were described for the first time and 12 were previously communicated for type 1 diabetes. Overall, 17 alleles were associated with type 1 diabetes and four with other forms of diabetes. Furthermore, three alleles were associated with obesity, five with adrenogenital diseases, three with hypoglycemia, and three with precocious puberty. Single alleles were found to be associated with congenital hypothyroidism without goiter, hyperfunction of pituitary gland, adrenomedullary hyperfunction, and short stature due to endocrine disorder. The study shows that early HLA typing can help detecting endocrine disorder genetic risk factors. In addition, associations with specific HLA alleles can broaden our understanding of the mechanisms of pathogenesis of relevant endocrine disorders.

A. Buzdin, Polina Pugacheva, Daniil V. Luppov et al.

M. Kamińska, M. Trofimiuk-Müldner, Grzegorz Sokołowski et al.

In recent years, endocrinology research has increasingly focused on machine learning (ML) applications. ML offers the possibility of utilizing large data sets and extracting imperceptible patterns. It might contribute in optimizing healthcare outcomes and unveiling new understandings of the intricate mechanisms of endocrine disorders. This review covers the basic aspects of ML and highlights specific areas of endocrinology with potential of ML application. This narrative review with a systematic literature search comprises studies on endocrine conditions with ML methods used in statistical analysis, published between January 2000 and December 2024. A total of 1130 studies were analyzed. Thyroid-related research was the most prevalent, followed by studies concerning the pituitary, adrenal and parathyroid glands. ML applications included medical imaging analysis, tumor classification, treatment response prediction, complication risk estimation and identification of molecular markers. ML has the potential to enhance the diagnosis, treatment and understanding of endocrine diseases. However, the use of ML is still limited by issues such as lack of model transparency, data imbalance and difficulties with clinical implementation. To enable safe and effective application of ML in endocrinology, further validation, interdisciplinary collaboration and standardized approaches are essential.

Yik Hin Chin, Zanariah Hussein

INTRODUCTIONMultiple endocrine neoplasia (MEN) syndromes are rare genetic disorders causing tumours in endocrine glands, with MEN 1 primarily affecting the parathyroid, pancreas, and pituitary, while MEN 2A is characterized by parathyroid tumour, medullary thyroid carcinoma (MTC) and pheochromocytoma. METHODOLOGYA retrospective cross-sectional study was conducted at Institut Endokrin Hospital Putrajaya. Electronic medical records of patients who attended Endocrinology or combined surgical clinic between 1st January 2015 till 31st March 2025 were reviewed. Descriptive and statistical analyses for MEN 1 and MEN 2A patients were performed using SPSS version 25. RESULTThe cohort comprised 16 patients with MEN syndromes—10 with MEN 1 and 6 with MEN 2A. There was a balanced gender distribution (56% male, 44% female), with an age range of 19 to 70 years (mean: 48 years). Patients with MEN 1 were slightly older (mean age: 49 years) compared to those with MEN 2A (mean age: 44 years). In the MEN 1 population, all had parathyroid involvement (100%), with 60% exhibiting pancreatic tumors and 30% adrenal or pituitary lesions. Most underwent parathyroid (80%) and pancreatic (60%) surgeries. Only 20% had family members screened for MEN genes, suggesting that family screening rate is still low. In the MEN 2A population, all cases featured medullary thyroid carcinoma (MTC) and RET oncogene mutations, with 83% found to have parathyroid disease and 50% with pheochromocytoma. Thyroid surgery was done for all patients while 83% underwent parathyroidectomy as well. Half had access to family genetic screening, emphasizing the hereditary nature of MEN 2A. CONCLUSIONDistinct profiles emerged from our cohort. Multiple endocrine neoplasia 1 is marked by parathyroid-pancreatic axis tumors, whereas MEN 2A is defined by MTC and RET mutations. A multidisciplinary approach, including genetic screening and tailored surgery, is critical for optimal outcomes. Genetic screening access for patient and family members can be improved to close the critical gaps in cascade testing for at-risk relatives.

Sarah Damanti, Sarah Damanti, Lorena Citterio et al.

BackgroundObesity and frailty are prevalent geriatric conditions that share some pathophysiological mechanisms and are associated with adverse clinical outcomes. The relationship between frailty, obesity, and polymorphism remains inadequately explored. Single nucleotide polymorphisms (SNPs) offer insights into genetic predispositions that may influence the development of both frailty and obesity.MethodsWe aimed at investigating whether SNPs associated with frailty also play a role in obesity. Data were collected from the FRASNET cross-sectional study, which included community-dwelling older individuals residing in Milan and nearby areas. Participants were recruited through random sampling. They underwent multidimensional geriatric assessments, which included the collection of blood samples for SNP analysis. Frailty was assessed using the frailty index, and body composition was evaluated using bioelectrical impedance analysis and anthropometric measures.ResultsSNPs related to frailty and linked to the renin–angiotensin system (CYP11B2 rs1799998, AGT rs5051, and AGTR1 rs2131127), apoptosis pathways (CASP8 rs6747918), growth hormone signaling (GHR rs6180), inflammation (TLR4 rs5030717, CD33 rs3865444, and FN1 rs7567647), adducin (ADD3 rs3731566), and the 9p21–23 region (rs518054) were found to be associated with various measures of obesity in community-dwelling older adults.ConclusionsFrailty-related SNPs contribute to obesity in community-dwelling older adults. We identified a novel association between adducin SNPs and visceral fat, which has not been previously reported. Detecting genetic predispositions to obesity and frailty early could aid in identifying individuals at risk, facilitating the adoption of preventive interventions. This represents an initial step toward promoting early intervention strategies.

Feng Zhang, Long Cheng

AimsThis research investigated menopausal women older than 50 years to find whether there were any independent relationships between the duration of sleep they got and their prevalence of depression.MethodsNational Health and Nutrition Examination Survey (NHANES) datasets from 2011-2020 were utilized in a cross-sectional study. Using multivariate linear regression models, the linear relationship between sleep duration and depression in menopausal women was investigated. Fitted smoothing curves and thresholds impact evaluation were used to investigate the nonlinear relationship. Then, subgroup analyses were performed according to smoking, drinking alcohol, diabetes, hypertension, heart disease, and moderate activities.ResultsThis population-based study included a total of 3,897 menopausal women (mean age 65.47 ± 9.06 years) aged≥50 years; 3,159 had a depression score &lt;10, and 738 had a depression score≥10. After controlling for all covariates, the prevalence of depression was 17% higher among participants with short sleep duration [OR=1.17, 95%CI=(0.65, 1.70), P&lt;0.0001] and 86% [OR=1.86, 95%CI=(1.05, 2.66), P&lt;0.0001] compared to participants with normal sleep duration. In subgroup analyses stratified by smoking and diabetes, the sleep duration and depression scores of non-smokers [β=-0.18, 95%CI= (-0.33, -0.02), P=0.0241] and diabetics were independently negatively correlated [β=-0.32, 95%CI= (-0.63, -0.01), P=0.0416]. Using a two-segment linear regression model, we discovered a U-shaped relationship between sleep duration and depression scores with an inflection point of 7.5 hours. Less than 7.5 hours of sleep was associated with an increased risk of developing depression [β=-0.81, 95%CI= (-1.05, -0.57), P&lt;0.001]. However, sleeping more than 7.5 hours per night increased the risk of depression considerably [β=0.80, 95%CI= (0.51, 1.08), P&lt;0.001].ConclusionsDepression is associated with sleep duration in menopausal women. Insufficient or excessive sleep may increase the risk of depression in menopausal women.

Vijaya Sarathi, Siddu Nikith

Kecheng Li, Kecheng Li, Xiaoli Zhou et al.

IntroductionBeta-amyloid accumulation in the brain appears to be a key initiating event in Alzheimer’s disease (AD), and factors associated with increased deposition of beta-amyloid are of great interest. Enhanced deposition of amyloid-β peptides is due to an imbalance between their production and elimination. Previous studies show that diminished levels of CSF amyloid beta 42 (Aβ42) is a biomarker in AD; however, the role of serum Aβ42 in AD is contradictory. BMI and obesity have been reported to be related to increased serum Aβ42 levels. Therefore, we aimed to investigate the relation between metabolic syndrome (MetS), its clinical measures (abdominal obesity, high glucose, high triglyceride, low high-density lipoprotein cholesterol level, and hypertension), and serum Aβ42 levels.MethodsA total of 1261 subjects, aged 18–89 years in Chengdu, China, were enrolled from January 2020 to January 2021 to explore the correlation of serum Aβ42 levels with body mass index (BMI), blood lipids, and blood pressure. Furthermore, as the risk of MetS is closely related to age, 1,212 participants (N = 49 with age ≥ 80 years old were excluded) were analyzed for the correlation of serum Aβ42 level and MetS clinical measures.ResultsThe results showed that log-transformed serum Aβ42 level was positively correlated with BMI (R = 0.29; p &lt; 0.001), log-transformed triglyceride (R = 0.14; p &lt; 0.001), and diastolic blood pressure (DBP) (R = 0.12; p &lt; 0.001) and negatively correlated with high-density lipoprotein (HDL-c) (R = −0.18; p &lt; 0.001). After adjusting for age, sex, and other covariates, elevated serum Aβ42 level was correlated with higher values of BMI (βmodel1 = 2.694, βmodel2 = 2.703) and DBP (βmodel1 = 0.541, βmodel2 = 0.546) but a lower level of HDL-c (βmodel2 = −1.741). Furthermore, serum Aβ42 level was positively correlated with MetS and its clinical measures, including BMI and DBP, and negatively correlated with HDL-c level in the Han Chinese population. However, the level of serum Aβ42 did not show a significant correlation with high glucose or high triglyceride.DiscussionThese observations indicate that MetS and its components are associated with higher levels of serum Aβ42 and hence limit the potential of serum Aβ42 as a suitable diagnostic biomarker for AD. As such, we recommend serum Aβ42 serve as a direct risk biomarker for MetS rather than for AD.

Ye Chen, Igor Couto, Wei Cai et al.

We introduce SoftTiger, a clinical large language model (CLaM) designed as a foundation model for healthcare workflows. The narrative and unstructured nature of clinical notes is a major obstacle for healthcare intelligentization. We address a critical problem of structuring clinical notes into clinical data, according to international interoperability standards. We collect and annotate data for three subtasks, namely, international patient summary, clinical impression and medical encounter. We then supervised fine-tuned a state-of-the-art LLM using public and credentialed clinical data. The training is orchestrated in a way that the target model can first support basic clinical tasks such as abbreviation expansion and temporal information extraction, and then learn to perform more complex downstream clinical tasks. Moreover, we address several modeling challenges in the healthcare context, e.g., extra long context window. Our blind pairwise evaluation shows that SoftTiger outperforms other popular open-source models and GPT-3.5, comparable to Gemini-pro, with a mild gap from GPT-4. We believe that LLMs may become a step-stone towards healthcare digitalization and democratization. Therefore, we publicly release SoftTiger models at scales of 13 billion and 70 billion parameters, as well as datasets and code for our innovative scalable evaluation, hopefully, making a significant contribution to the healthcare industry.

Jon Z. Cai, Kristin Wright-Bettner, Martha Palmer et al.

This paper is dedicated to the design and evaluation of the first AMR parser tailored for clinical notes. Our objective was to facilitate the precise transformation of the clinical notes into structured AMR expressions, thereby enhancing the interpretability and usability of clinical text data at scale. Leveraging the colon cancer dataset from the Temporal Histories of Your Medical Events (THYME) corpus, we adapted a state-of-the-art AMR parser utilizing continuous training. Our approach incorporates data augmentation techniques to enhance the accuracy of AMR structure predictions. Notably, through this learning strategy, our parser achieved an impressive F1 score of 88% on the THYME corpus's colon cancer dataset. Moreover, our research delved into the efficacy of data required for domain adaptation within the realm of clinical notes, presenting domain adaptation data requirements for AMR parsing. This exploration not only underscores the parser's robust performance but also highlights its potential in facilitating a deeper understanding of clinical narratives through structured semantic representations.