Search Results (1,654)

Background and Objectives: Accurately diagnosing acute appendicitis (AA) in children remains clinically challenging due to overlapping symptoms with other pediatric conditions and limitations in conventional diagnostic tools. The systemic immune-inflammation index (SII) has emerged as a promising biomarker in adult populations; however, its utility in pediatrics is still unclear. This study aimed to evaluate the diagnostic accuracy of SII in distinguishing pediatric acute appendicitis from elective non-inflammatory surgical procedures and to assess its predictive value in identifying complicated cases. Materials and Methods: This retrospective, single-center study included 397 pediatric patients (5–15 years), comprising 297 histopathologically confirmed appendicitis cases and 100 controls. Demographic and laboratory data were recorded at admission. Inflammatory indices including SII, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were calculated. ROC curve analysis was performed to evaluate diagnostic performance. Results: SII values were significantly higher in the appendicitis group (median: 2218.4 vs. 356.3; p < 0.001). SII demonstrated excellent diagnostic accuracy for AA (AUROC = 0.95, 95% CI: 0.92–0.97), with 91% sensitivity and 88% specificity at a cut-off > 624. In predicting complicated appendicitis, SII showed moderate discriminative ability (AUROC = 0.66, 95% CI: 0.60–0.73), with 83% sensitivity but limited specificity (43%). Conclusions: SII is a reliable and easily obtainable biomarker for diagnosing pediatric acute appendicitis and may aid in early detection of complicated cases. Its integration into clinical workflows may enhance diagnostic precision, particularly in resource-limited settings. Age-specific validation studies are warranted to confirm its broader applicability. Full article

Objectives: Febrile neutropenia is a common oncologic emergency with significant morbidity and mortality. Although the MASCC (Multinational Association for Supportive Care in Cancer) score is widely used for risk stratification, its limited sensitivity and lack of laboratory parameters reduce its prognostic utility. This study aimed to evaluate whether incorporating serum lactate and CRP measurements into the MASCC score enhances its predictive performance for hospital admission and the 30-day mortality. Methods: This retrospective diagnostic accuracy study included adult patients diagnosed with febrile neutropenia in the emergency department of a tertiary care hospital between January 2021 and December 2024. The original MASCC score was calculated, and three modified models were derived: the MASCC-L (lactate/MASCC), MASCC-C (CRP/MASCC) and MASCC-LC models (CRP × lactate/MASCC). The predictive accuracy for hospital admission and the 30-day all-cause mortality was assessed using ROC analysis. Results: A total of 269 patients (mean age: 67.6 ± 12.4 years) were included; the 30-day mortality was 3.0%. The MASCC-LC model demonstrated the highest discriminative ability for mortality prediction (area under the curve (AUC): 0.995; sensitivity: 100%; specificity: 98%). For hospital admission prediction, the MASCC-C model had the highest specificity (81%), while the MASCC-LC model showed the best balance of sensitivity and specificity (both 73%). All the modified models outperformed the original MASCC score regarding both endpoints. Conclusions: Integrating lactate and CRP measurements into the MASCC score significantly improves its prognostic accuracy for both mortality and hospital admission in febrile neutropenic patients. The MASCC-LC model, relying on only three objective parameters, may serve as a practical and efficient tool for early risk stratification in emergency settings. Full article

Background: Pigmented superficial skin lesions pose a persistent diagnostic challenge due to overlapping clinical and dermoscopic appearances between benign and malignant entities. While histopathology remains the gold standard, there is growing interest in non-invasive imaging models that can preoperatively stratify malignancy risk. This methodological pilot study was designed to explore the feasibility and initial diagnostic performance of a novel radiology-adapted logistic regression approach. To develop and preliminarily evaluate a new logistic model integrating both structural (lesion size, depth) and vascular (Doppler patterns) ultrasonographic features for non-invasive risk stratification of pigmented superficial skin lesions. Material and Methods: In this prospective single-center pilot investigation, 44 patients underwent standardized high-frequency grayscale and Doppler ultrasound prior to excisional biopsy. Lesion size, depth, and vascularity patterns were systematically recorded. Three logistic regression models were constructed: (1) based on lesion size and depth, (2) based on vascularity patterns alone, and (3) combining all parameters. Model performance was assessed via ROC curve analysis. Intra-observer reliability was determined by repeated measurements on a random subset. Results: The lesion size and depth model yielded an AUC of 0.79, underscoring the role of structural features. The vascularity-only model showed an AUC of 0.76. The combined model demonstrated superior discriminative ability, with an AUC of approximately 0.85. Intra-observer analysis confirmed excellent repeatability (κ > 0.80; ICC > 0.85). Conclusions: This pilot study introduces a novel logistic framework that combines grayscale and Doppler ultrasound parameters to enhance non-invasive malignancy risk assessment in pigmented superficial skin lesions. These encouraging initial results warrant larger multicenter studies to validate and refine this promising approach. Full article

Background/Objectives: Malnutrition is a common problem in children with cerebral palsy (CP). The aim of this study was to investigate the suitability and diagnostic performance of mid-upper arm circumference (MUAC) z-score in diagnosing wasting in children with CP, and its impact on diagnostic accuracy when evaluated concomitantly with additional clinical factors (birth weight, history of phototherapy). Methods: This single-center, cross-sectional study included 83 children with CP, aged 6 months–17 years, followed-up in our clinic. Anthropometric measurements (MUAC, Body Mass Index (BMI)) and clinical data (birth weight, history of phototherapy, Gross Motor Function Classification System (GMFCS)) were prospectively collected. Wasting was defined according to the BMI z-score ≤ −2 criteria. The diagnostic performance of MUAC z-score was evaluated by Receiver Operating Characteristic (ROC) analysis. The contribution of additional covariates was examined using logistic regression analysis and the backward elimination method. Results: MUAC z-score alone demonstrated good discrimination in diagnosing wasting with an Area Under the Curve (AUC) value between 0.805 and 0.821, but its sensitivity was limited (67.0%). No statistically significant difference was found in diagnostic performance between MUAC measurements of the right arm, left arm, and the unaffected arm (p > 0.050). In logistic regression analysis, MUAC z-score (p = 0.001), birth weight (p = 0.014), and a history of phototherapy (p = 0.046) were found to be significantly associated with wasting malnutrition. The simplified model including these variables yielded an AUC value of 0.876. Conclusions: MUAC z-score is a usable tool for wasting malnutrition screening in children with CP. Although its sensitivity is limited when used alone, its diagnostic accuracy increases when evaluated concomitantly with additional clinical factors such as birth weight and a history of phototherapy. This combined approach may offer clinicians a more robust tool for the early diagnosis and management of wasting malnutrition in children with CP. Full article

Purpose: This study aimed to develop a predictive nomogram integrating AI-based BI-RADS lexicons and lesion-to-nipple distance (LND) ultrasound features to differentiate mass-type ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) visible on ultrasound. Methods: The final study cohort consisted of 170 women with 175 pathologically confirmed malignant breast lesions, including 26 cases of DCIS and 149 cases of IDC. LND and AI-based features from the S-Detect system (BI-RADS lexicons) were analyzed. Rare features were consolidated into broader categories to enhance model stability. Data were split into training (70%) and validation (30%) sets. Logistic regression identified key predictors for an LND nomogram. Model performance was evaluated using receiver operating characteristic (ROC) curves, 1000 bootstrap resamples, and calibration curves to assess discrimination and calibration. Results: Multivariate logistic regression identified smaller lesion size, irregular shape, LND ≤ 3 cm, and non-hypoechoic echogenicity as independent predictors of DCIS. These variables were integrated into the LND nomogram, which demonstrated strong discriminative performance (AUC = 0.851 training; AUC = 0.842 validation). Calibration was excellent, with non-significant Hosmer-Lemeshow tests (p = 0.127 training, p = 0.972 validation) and low mean absolute errors (MAE = 0.016 and 0.034, respectively), supporting the model’s accuracy and reliability. Conclusions: The AI-based comprehensive nomogram demonstrates strong reliability in distinguishing mass-type DCIS from IDC, offering a practical tool to enhance non-invasive breast cancer diagnosis and inform preoperative planning. Full article

Background/Objectives: Peripheral artery disease (PAD) impacts more than 200 million individuals globally and leads to mortality and morbidity secondary to progressive limb dysfunction and amputation. However, clinical management of PAD remains suboptimal, in part because of the lack of standardized biomarkers to predict patient outcomes. Growth differentiation factor 15 (GDF15) is a stress-responsive cytokine that has been studied extensively in cardiovascular disease, but its investigation in PAD remains limited. This study aimed to use explainable statistical and machine learning methods to assess the prognostic value of GDF15 for limb outcomes in patients with PAD. Methods: This prognostic investigation was carried out using a prospectively enrolled cohort comprising 454 patients diagnosed with PAD. At baseline, plasma GDF15 levels were measured using a validated multiplex immunoassay. Participants were monitored over a two-year period to assess the occurrence of major adverse limb events (MALE), a composite outcome encompassing major lower extremity amputation, need for open/endovascular revascularization, or acute limb ischemia. An Extreme Gradient Boosting (XGBoost) model was trained to predict 2-year MALE using 10-fold cross-validation, incorporating GDF15 levels along with baseline variables. Model performance was primarily evaluated using the area under the receiver operating characteristic curve (AUROC). Secondary model evaluation metrics were accuracy, sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). Prediction histogram plots were generated to assess the ability of the model to discriminate between patients who develop vs. do not develop 2-year MALE. For model interpretability, SHapley Additive exPlanations (SHAP) analysis was performed to evaluate the relative contribution of each predictor to model outputs. Results: The mean age of the cohort was 71 (SD 10) years, with 31% (n = 139) being female. Over the two-year follow-up period, 157 patients (34.6%) experienced MALE. The XGBoost model incorporating plasma GDF15 levels and demographic/clinical features achieved excellent performance for predicting 2-year MALE in PAD patients: AUROC 0.84, accuracy 83.5%, sensitivity 83.6%, specificity 83.7%, PPV 87.3%, and NPV 86.2%. The prediction probability histogram for the XGBoost model demonstrated clear separation for patients who developed vs. did not develop 2-year MALE, indicating strong discrimination ability. SHAP analysis showed that GDF15 was the strongest predictive feature for 2-year MALE, followed by age, smoking status, and other cardiovascular comorbidities, highlighting its clinical relevance. Conclusions: Using explainable statistical and machine learning methods, we demonstrated that plasma GDF15 levels have important prognostic value for 2-year MALE in patients with PAD. By integrating clinical variables with GDF15 levels, our machine learning model can support early identification of PAD patients at elevated risk for adverse limb events, facilitating timely referral to vascular specialists and aiding in decisions regarding the aggressiveness of medical/surgical treatment. This precision medicine approach based on a biomarker-guided prognostication algorithm offers a promising strategy for improving limb outcomes in individuals with PAD. Full article

Background and Objectives: Accurate noninvasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) remains a clinical challenge. This study aimed to assess the dignostic performance of apparent diffusion coefficient (ADC) values from diffusion-weighted MRI in distinguishing between HCC and ICC, with histological confirmation as the gold standard. Materials and Methods: A retrospective analysis was performed on 61 patients (41 HCC, 20 ICC) who underwent liver MRI and percutaneous biopsy between 2019 and 2024. ADC values were measured from diffusion-weighted sequences (b-values of 0, 500, and 1000 s/mm²), and regions of interest were placed over solid tumor areas. Statistical analyses included t-tests, one-way ANOVA, and ROC curve analysis. Results: Mean ADC values did not differ significantly between HCC (1.09 ± 0.19 × 10⁻³ mm²/s) and ICC (1.08 ± 0.11 × 10⁻³ mm²/s). ROC analysis showed poor discriminative ability (AUC = 0.520; p = 0.806). In HCC, ADC values decreased with lower differentiation grades (p = 0.008, η² = 0.224). No significant trend was observed in ICC (p = 0.410, η² = 0.100). Immunohistochemical markers such as CK-7, Glypican 3, and TTF-1 showed significant diagnostic value between tumor subtypes. Conclusions: ADC values have limited utility for distinguishing HCC from ICC but may aid in HCC grading. Immunohistochemistry remains essential for accurate diagnosis, especially in poorly differentiated tumors. Further studies with larger cohorts are recommended to improve noninvasive diagnostic protocols. Full article

Objectives: To identify clinical, functional, laboratory, and patient-reported parameters associated with medium-term risk of hospitalization or death among older adults attending a multidisciplinary outpatient clinic, and to assess the predictive performance of these measures for individual risk stratification. Methods: In this cohort study, 350 adults aged ≥65 years were assessed at baseline and followed for an average of 8 months. The primary outcome was a composite of hospitalization or all-cause mortality. Parameters assessed included frailty and comorbidity measures, functional parameters, such as gait speed and grip strength, laboratory biomarkers, and patient-reported measures, such as quality of life (QoL, assessed on a Likert scale) and the presence of depressive symptoms. Predictive performance was evaluated using univariable logistic regression and multivariable modeling. Discriminative ability was assessed via area under the ROC curve (AUC), and selected models were internally validated using repeated k-fold cross-validation. Results: Overall, 40 participants (11.4%) experienced hospitalization or death. Traditional clinical risk indicators, including frailty and comorbidity scores, were significantly associated with the outcome. Patient-reported QoL (AUC = 0.74) and Geriatric Depression Scale (GDS) scores (AUC = 0.67) demonstrated useful overall discriminatory ability, with high specificities at optimal cut-offs, suggesting they could act as “red flags” for adverse outcomes. However, the limited sensitivities of individual predictors underscore the need for more comprehensive screening instruments with improved ability to identify at-risk individuals earlier. A multivariable model that incorporated several predictors did not outperform QoL alone (AUC = 0.79), with cross-validation confirming comparable discriminative performance. Conclusions: Patient-reported measures—particularly quality of life and depressive symptoms—are valuable predictors of hospitalization or death and may enhance traditional frailty and comorbidity assessments in outpatient geriatric care. Future work should focus on developing or integrating screening tools with greater sensitivity to optimize early risk detection and guide preventive interventions. Full article

Objective: To evaluate the prognostic utility of the Sequential Organ Failure Assessment (SOFA) and Simplified Acute Physiology Score 3 (SAPS 3) in COVID-19 patients and assess whether incorporating C-reactive protein (CRP), procalcitonin, lactate, and lactate dehydrogenase (LDH) enhances their predictive accuracy. Methods: Single-center, observational, cohort study. We analyzed a database of adult ICU patients with severe or critical COVID-19 treated at a large academic center. We used binary logistic regression for all analyses. We assessed the predictive performance of SAPS 3 and SOFA scores within 24 h of admission, individually and in combination with serum lactate, LDH, CRP, and procalcitonin. We examined the independent association of these biomarkers with hospital mortality. We evaluated discrimination using the C-statistic and determined clinical utility with decision curve analysis. Results: We included 1395 patients, 66% of whom required mechanical ventilation, and 59.7% needed vasopressor support. Patients who died (39.7%) were significantly older (61.1 ± 15.9 years vs. 50.1 ± 14.5 years, p < 0.001) and had more comorbidities than survivors. Among the biomarkers, only procalcitonin was independently associated with higher mortality in the multivariable analysis, in a non-linear pattern. The AUROC for predicting hospital mortality was 0.771 (95% CI: 0.746–0.797) for SAPS 3 and 0.781 (95% CI: 0.756–0.805) for the SOFA score. A model incorporating the SOFA score, age, and procalcitonin demonstrated high AUROC of 0.837 (95% CI: 0.816–0.859). These associations with the SOFA score showed greater clinical utility. Conclusions: The SOFA score may aid clinical decision-making, and incorporating procalcitonin and age could further enhance its prognostic utility. Full article

The automated detection and classification of immature macauba (Acrocomia aculeata) fruits is critical for improving post-harvest processing and quality control. In this study, we present a comparative evaluation of two state-of-the-art YOLO architectures, YOLOv11x and YOLOv12x, trained on the newly constructed VIC01 dataset comprising 1600 annotated images captured under both background-free and natural background conditions. Both models were implemented in PyTorch and trained until the convergence of box regression, classification, and distribution-focal losses. Under an IoU (intersection over union) threshold of 0.50, YOLOv11x and YOLOv12x achieved an identical mean average precision (mAP₅₀) of 0.995 with perfect precision and recall or TPR (true positive rate). Averaged over IoU thresholds from 0.50 to 0.95, YOLOv11x demonstrated superior spatial localization performance (mAP_50–95 = 0.973), while YOLOv12x exhibited robust performance in complex background scenarios, achieving a competitive mAP_50–95. Inference throughput averaged 3.9 ms per image for YOLOv11x and 6.7 ms for YOLOv12x, highlighting a trade-off between speed and architectural complexity. Fused model representations revealed optimized layer fusion and reduced computational overhead (GFLOPs), facilitating efficient deployment. Confusion-matrix analyses confirmed YOLOv11x’s ability to reject background clutter more effectively than YOLOv12x, whereas precision–recall and F1-score curves indicated both models maintain near-perfect detection balance across thresholds. The public release of the VIC01 dataset and trained weights ensures reproducibility and supports future research. Our results underscore the importance of selecting architectures based on application-specific requirements, balancing detection accuracy, background discrimination, and computational constraints. Future work will extend this framework to additional maturation stages, sensor fusion modalities, and lightweight edge-deployment variants. By facilitating precise immature fruit identification, this work contributes to sustainable production and value addition in macauba processing. Full article

To clarify the potential failure modes of reinforced concrete (RC) beams under impact and understand their impact resistance safety, a comprehensive study was conducted by focusing on the failure mode discrimination and failure probability of RC beams under impact loads. This research utilized drop hammer impact tests, ABAQUS2022 software, and theoretical methods. The study examined three typical failure modes of RC beams under impact loads: flexural failure, flexural-shear failure, and shear failure. A discrimination criterion based on the flexural-shear capacity–effect curve was developed. Utilizing this criterion, along with the basic principles of structural reliability theory, the failure probability of RC beams under impact loads was calculated and analyzed using the Monte Carlo method. The results indicate that the criterion based on the flexural-shear capacity–effect curve can be used for discriminating failure modes of RC beams under impact loads. The impact velocity and stirrup ratio were identified as crucial factors that influenced the failure modes of RC beams under impact. Specifically, an increase in the stirrup spacing reduced the reliability of the RC beams under impact, while an increase in the stirrup ratio could significantly enhance their impact resistance. Furthermore, with a constant impact energy, an increase in beam span correlated with the improved reliability of RC beams under impact, where larger spans yielded a better impact resistance. Full article

Hair, as a non-invasive biospecimen, retains metabolic deposits from sebaceous glands and capillaries, reflecting substances from the peripheral circulation, and provides valuable biochemical information linked to phenotypes, yet its application in animal disease research remains limited. This work applied ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) to compare the hair metabolomic characteristics of healthy forest musk deer (FMD, Moschus berezovskii) and those diagnosed with hemorrhagic pneumonia (HP), phytobezoar disease (PD), and abscess disease (AD). A total of 2119 metabolites were identified in the FMD hair samples, comprising 1084 metabolites in positive ion mode and 1035 metabolites in negative ion mode. Differential compounds analysis was conducted utilizing the orthogonal partial least squares–discriminant analysis (OPLS-DA) model. In comparison to the healthy control group, the HP group displayed 85 upregulated and 92 downregulated metabolites, the PD group presented 124 upregulated and 106 downregulated metabolites, and the AD group exhibited 63 upregulated and 62 downregulated metabolites. Functional annotation using the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that the differential metabolites exhibited significant enrichment in pathways associated with cancer, parasitism, energy metabolism, and stress. Receiver operating characteristic (ROC) analysis revealed that both the individual and combined panels of differential metabolites exhibited area under the curve (AUC) values exceeding 0.7, demonstrating good sample discrimination capability. This research indicates that hair metabolomics can yield diverse biochemical insights and facilitate the development of non-invasive early diagnostic techniques for diseases in captive FMD. Full article

Introduction: The figure-of-eight walk test (F8WT) assesses gait on a curved path, reflecting everyday walking complexity. Despite recognized validity among elderly individuals, its application in neurological disorders remains inadequately explored. This scoping review summarizes evidence regarding F8WT use, validity, and clinical applicability among individuals with neurological disorders. Methods: A systematic literature search was conducted in the PubMed, Scopus, Embase, and Web of Science databases. After reading the full text of the selected studies and applying predefined inclusion criteria, seven studies, involving participants with multiple sclerosis (n = 3 studies), Parkinson’s disease (n = 2 studies), and stroke (n = 2 studies), were included based on pertinence and relevance to the topic. Results: F8WT demonstrated strong reliability and validity across various neurological populations and correlated significantly with established measures of gait, balance, and disease severity. Preliminary evidence supports its ability to discriminate individuals at increased fall risk and detect subtle motor performance changes. Discussion: The F8WT emerges as a valuable tool, capturing multifaceted gait impairments often missed by linear walking assessments. Sensitive to subtle functional changes, it is suitable for tracking disease progression and intervention efficacy. Conclusions: F8WT is reliable and clinically relevant, effectively identifying subtle, complex walking impairments in neurological disorders. Full article

Background: Autoimmune liver diseases (ALDs) are a diverse group of chronic inflammatory disorders. Individuals with a history of one autoimmune disease (AD) are at a substantially increased risk of developing additional autoimmune conditions. Polyautoimmunity has increasingly been recognized as a factor associated with a more complex disease course and poorer long-term outcomes. Methods: This retrospective, cross-sectional observational study reviewed medical records of patients diagnosed with ALDs who had been admitted to the gastroenterology clinic. Results: A total of 457 patients with ALDs were included. Polyautoimmunity was present in 194 patients (42.5%), and multiple autoimmune syndrome (MAS) was diagnosed in 26 of these patients (5.7%). Serological comparisons revealed that antinuclear antibody (ANA) positivity was significantly more common in the polyautoimmunity group. Only 22.2% of the patients with polyautoimmunity were ANA-negative, compared with 52.9% in those without. An ROC curve analysis was conducted to assess the predictive value of clinical and laboratory variables for polyautoimmunity. Among all the tested parameters, ANA positivity (>+2) had the strongest predictive value (AUC: 0.724). A disease duration longer than 6.5 years followed, with a moderate discriminative capacity (AUC: 0.677). Additionally, lower albumin levels (<3.0 g/dL) and elevated erythrocyte sedimentation rates (ESRs) (>29.5 mm/h) were significantly associated with polyautoimmunity. Conclusions: In our cohort, 42.5% of patients had at least one additional autoimmune disorder, highlighting the need for a systemic and comprehensive approach to patient care. Simple and accessible markers—such as ANA titers, disease duration, albumin levels, and ESRs—may help to identify patients at greater risk. Full article

Background/Objectives: Falls in hospitalized pediatric patients are frequent and can lead to serious complications and increased healthcare costs. Nurses typically assess fall risk using structured tools such as the Humpty Dumpty Fall Scale (HDFS), alongside nursing diagnoses such as Fall risk ND, which are based on clinical reasoning. However, the degree of alignment between the HDFS and the nursing reasoning-based diagnostic approach in assessing fall risk remains unclear. This study aims to assess the alignment between the HDFS and Fall risk ND in identifying fall risk among hospitalized pediatric patients. Methods: A retrospective observational study was conducted in a tertiary pediatric hospital in Italy, including all pediatric patients admitted in 2022. Fall risk was assessed within 24 h from hospital admission using two approaches, the HDFS (risk identified with the standard cutoff, score ≥ 12) and Fall risk ND, based on the nurse’s clinical reasoning and recorded through the PAIped clinical nursing information system. Discriminative performance was analyzed using receiver operating characteristic curve analysis. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. A confusion matrix evaluated classification performance at the cutoff (≥12). Results: Among 2086 inpatients, 80.9% had a recorded Fall risk ND. Of the 1853 patients assessed with the HDFS, 52.7% were classified as at risk (HDFS score ≥ 12). The HDFS showed low discriminative ability in detecting patients with a Fall risk ND (AUC = 0.568; 95% CI: 0.535−0.602). The PPV was high (85.1%), meaning that most patients identified as at risk by the HDFS were also judged to be at risk by nurses through Fall risk ND. However, the NPV was low (20.1%), indicating that many patients with low HDFS scores were still diagnosed with Fall risk ND by nurses. Conclusions: The HDFS shows limited ability to discriminate pediatric patients with Fall risk ND, capturing a risk profile that does not fully align with nursing clinical reasoning. This suggests that standardized tools and clinical reasoning address distinct yet complementary dimensions of fall risk assessment. Integrating the HDFS into a structured nursing diagnostic process—guided by clinical expertise and supported by continuous education—can strengthen the effectiveness of fall prevention strategies and enhance patient safety in pediatric settings. Full article