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Draft a comprehensive differential diagnosis for acute substernal chest pain in a 45-year-old male with hypertension and hyperlipidemia, diaphoresis, exertional onset, and normal initial ECG/troponin; include life-threatening causes first, key distinguishing features, and immediate diagnostic next steps.

Draft a detailed differential diagnosis for new-onset unilateral throbbing headache with visual aura and brief monocular vision loss in a 32-year-old female on oral contraceptives; include red-flag symptoms, stroke/TIA considerations, and recommended initial workup.

Draft an assessment and plan for stage-2 hypertension (BP 162/98) in an obese 52-year-old with suspected obstructive sleep apnea; include ASCVD risk estimate, lifestyle prescriptions, first-line pharmacotherapy with drug/dose/targets, baseline labs/monitoring, and 4–6 week follow-up.

Draft an assessment and plan for type 2 diabetes follow-up with A1c 9.2% on metformin 1,000 mg BID and eGFR 65; compare adding a GLP-1 receptor agonist vs an SGLT2 inhibitor, address weight loss goals, hypoglycemia education, vaccinations, and micro/macrovascular screening.

Draft a history and physical for a patient presenting with chest pain, including HPI using OLDCARTS, pertinent ROS, PMH/meds/allergies, FH/SH, focused cardiac/pulmonary exam, initial labs and imaging orders, prioritized differential, HEART score risk stratification, and early management.

Draft a history and physical for a new patient with progressive exertional dyspnea, orthopnea, and bilateral leg edema; include differential (heart failure, COPD, PE, anemia, renal disease), targeted exam maneuvers, and first-line diagnostics and management.

Draft a progress note for COPD follow-up two weeks after an acute exacerbation treated with prednisone and antibiotics; include symptom trajectory, CAT score (if available), inhaler technique/adherence, oxygen needs, spirometry plan, smoking cessation counseling, and return precautions.

Draft a progress note for post-operative day 2 after total knee arthroplasty; include pain control strategy, DVT prophylaxis, PT/OT milestones, wound status, bowel/urinary function, labs/vitals review, complications screening, and discharge readiness.

Draft a discharge summary for community-acquired pneumonia admission; include admission/discharge dates, presenting symptoms, radiology/labs, pathogen if identified, inpatient therapies and response, discharge medications with durations, pending tests, follow-up appointments, and return precautions.

Draft a discharge summary for myocardial infarction managed with PCI and drug-eluting stent placement; include cath findings, complications, inpatient course, discharge medications (DAPT, statin, beta-blocker, ACEi/ARB), cardiac rehab referral, and follow-up plan.

Draft discharge instructions for a patient hospitalized with acute decompensated heart failure; include daily weights, sodium and fluid restriction amounts, medication list with dosing, signs of worsening heart failure, follow-up appointments, and lab monitoring plan.

Draft discharge instructions for an asthma exacerbation; include controller and reliever regimen, steroid taper schedule, spacer technique, trigger avoidance, a written asthma action plan, return precautions, and follow-up timing.

Draft a patient handout on managing high blood pressure in plain language; explain what BP numbers mean, key lifestyle steps (DASH diet, sodium limits, alcohol moderation), home BP monitoring instructions, medication adherence tips, and when to call the doctor.

Draft a patient handout for type 2 diabetes education in plain language; cover A1c goals, the plate method, physical activity targets, self-monitoring of blood glucose, hypoglycemia signs and the 15-15 rule, foot care basics, and a vaccination checklist.

Draft a summary of the latest CDC and WHO guidelines for managing community-acquired pneumonia in adults, including empiric antibiotic choices, duration of therapy, and follow-up recommendations.

Draft a plain-language overview of the ADA and AACE guidelines for diabetes management, focusing on A1c goals, weight management, and the use of GLP-1 receptor agonists and SGLT2 inhibitors.

Draft a summary of the 2024 USPSTF breast cancer screening recommendations, including updated screening intervals, risk-based approaches, and the role of MRI for high-risk patients.

Draft a concise summary of NCCN guidelines for melanoma management, including staging, surgical margins, adjuvant therapy, and checkpoint inhibitor recommendations.

Draft treatment alternatives for community-acquired MRSA skin infections in patients allergic to sulfa drugs; include oral and IV options, dosing, and duration.

Draft treatment alternatives for patients with type 2 diabetes who are unable to tolerate GLP-1 receptor agonists due to nausea; include medication substitutions and lifestyle recommendations.

Draft a summary of options for treating refractory depression, comparing ketamine infusions, transcranial magnetic stimulation (TMS), and electroconvulsive therapy (ECT).

Draft treatment options for advanced prostate cancer, including hormonal therapy, radiopharmaceuticals, and novel androgen receptor pathway inhibitors.

Draft a summary of recent research on mRNA vaccine technology beyond COVID-19, including applications in oncology and autoimmune disease prevention.

Draft an easy-to-understand overview of long COVID research, summarizing major symptoms, hypothesized mechanisms, and emerging treatment approaches.

Draft a summary of current research on Alzheimer’s disease biomarkers, including amyloid and tau imaging, and discuss how they influence diagnosis and trial design.

Draft an explanation of the interaction between warfarin and antibiotics such as ciprofloxacin or TMP-SMX, including INR effects and management recommendations.

Draft a summary evaluating drug interactions for patients taking Paxlovid and common cardiovascular drugs; include mechanism and recommended dose adjustments.

Draft a summary of the common and serious side effects of semaglutide, including GI intolerance, gallbladder disease, and thyroid-related warnings.

Draft an overview of potential side effects associated with long-term corticosteroid use, including osteoporosis, glucose intolerance, and adrenal suppression.

Draft a summary of essential lab tests to order in suspected acute pancreatitis, including amylase, lipase, liver enzymes, and metabolic panels, with interpretation guidance.

Draft laboratory workup recommendations for suspected hypothyroidism, including TSH, free T4, thyroid antibodies, and when to consider secondary causes.

Draft an explanation of which labs should be monitored in patients receiving methotrexate therapy, including CBC, LFTs, renal function, and frequency of monitoring.

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Research Article: Tau protein mediates the association between frailty and postoperative delirium: a machine learning model incorporating cerebrospinal fluid biomarkers

Date Published: 2025-09-17

Abstract:
Postoperative delirium (POD) is a prevalent neurological complication linked to adverse clinical outcomes. The underlying mechanisms of POD remain unclear. This study aimed to investigate the association between POD and frailty and determine whether frailty influences POD incidence. Furthermore, machine learning algorithms were utilized to identify key predictors of POD in patients undergoing hip or knee replacement. A total of 625 Han Chinese patients were recruited between September 2021 and May 2023. Preoperative frailty was assessed using the Frailty Scale and Frailty Phenotype criteria. The Mini-Mental State Examination (MMSE) evaluated preoperative cognitive function, while the Confusion Assessment Method (CAM) diagnosed POD. The severity of POD was additionally quantified using the Memorial Delirium Assessment Scale (MDAS). Receiver Operating Characteristic (ROC) curve analysis explored the association between preoperative frailty and POD, and the mediating effect of cerebrospinal fluid (CSF) biomarkers was analyzed. Ten machine learning algorithms—including Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Artificial Neural Network (ANN), Random Forest (RF), XGBoost, K-Nearest Neighbors (KNN), AdaBoost, LightGBM, and CatBoost—were implemented to develop predictive models. The dataset was randomly split into training (70%) and testing (30%) subsets. Ten-fold cross-validation was incorporated during model training and validation to mitigate overfitting and enhance generalizability. Model performance was evaluated using multiple metrics, such as accuracy, sensitivity, specificity, precision, Brier score, area under the ROC curve (AUC), and F1 score. Furthermore, graphical analyses—including calibration curves, decision diagrams, clinical impact curves, and confusion matrices—were applied to assess model robustness and clinical utility. Finally, SHAP (Shapley Additive Explanations) analysis elucidated the model’s decision-making process, emphasizing the pivotal role of preoperative frailty in POD prediction. The incidence of POD was 14.7%. The study identified frailty, Tau, and P-tau as significant risk factors for POD (OR?=?67.229, 95% CI: 34.649–130.444, p <?0.001; OR?=?1.020, 95% CI: 1.016–1.024, p <?0.001; OR?=?1.018, 95% CI: 1.010–1.027, p <?0.001). ROC curve analysis (AUC?=?0.983) demonstrated that combining frailty with CSF biomarkers had strong predictive power for distinguishing POD. The direct effect of frailty on POD was 0.504878, the total effect was 0.6547619, and the mediating effect of Tau accounted for 22.89%. Using Lasso regression for variable selection, we subsequently identified eight predictors—frailty, Tau, A?42/Tau, A?40, age, A?42, P-tau, and drinking history—from the training set via logistic regression. Based on these factors, we constructed 10 machine learning models. Among all machine learning algorithms, GBM performed the best, achieving an AUC of 0.973 (95% CI, 0.973–1.000) in the test set. Furthermore, SHAP analysis confirmed that frailty and Tau were the key determinants influencing the machine learning model’s predictions. Preoperative frailty is an independent risk factor for POD. A machine learning model for predicting POD in patients undergoing hip or knee replacement was developed, with GBM demonstrating superior performance among all models. The GBM-based model enabled early identification of patients at high risk of delirium.

Introduction:
POD is an acute disturbance in attention and cognition, primarily observed in older adults. It is associated with severe outcomes, high healthcare costs, underdiagnosis, and increased mortality risk ( 1 ). As life expectancy increases, the number of older surgical patients has risen correspondingly. POD is the most common postoperative complication in this population, with an incidence ranging from 15 to 25% ( 2 ). Currently, the pathophysiological mechanisms of delirium remain unclear and may involve multiple…

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