📄 ER Order Sheet: Suspected Alcohol Intoxication 🧾 Initial Orders DTX (capillary blood glucose) – stat Vital signs monitoring Observe in ER (serial mental status + airway monitoring) 💉 Medications Thiamine 100 mg IV stat OR Vitamin B complex (containing thiamine 100 mg) IV stat 💧 IV Fluids (ONLY if indicated) 0.9% NSS 1000 mL IV Rate: 80–100 mL/hr OR adjust based on clinical status Indication: dehydration / vomiting / poor oral intake 🧪 Labs (ONLY if clinically indicate

Tonsillitis Management Sheet Situation Setting Key findings Treatment Follow-up / next step Likely viral tonsillitis OPD mild sore throat, cough/rhinorrhea present, able to swallow, no red flags No antibiotic . Supportive care: Paracetamol (500 mg), 1–2 tab po q6h prn , warm saline gargle, hydration, rest Return if worse, high fever, cannot swallow, unilateral swelling Likely bacterial tonsillitis (GAS pattern) OPD fever, tonsillar exudate, tender anterior cervical nodes, no

Introduction A lot of people get stuck the first time they see this in Stata: power twomeans m1 m2, ... The syntax says means , so it feels natural to think m1 and m2 must always be the actual mean outcome in two groups . That is true in many ordinary superiority studies. But it is not the whole story. In practice, researchers sometimes enter values like 0 and 2 in a non-inferiority design and still get a valid sample size calculation. At first glance, that looks wrong. Why w

Focus: Fingernail vs Toenail 1) Diagnosis Onychomycosis = fungal infection of the nail (dermatophytes most common) Clinical features Yellow / white discoloration Thickened nail Subungual debris Onycholysis (nail lifting) Starts distal → proximal Confirmation (IMPORTANT before long treatment) KOH preparation Fungal culture Nail clipping + PAS stain 👉 Exam pearl: Not every abnormal nail = fungus → confirm before oral antifungal 2) Pathophysiology Fungus invades nail bed → nail

What is Feature Importance? Feature importance answers the question: “Which predictors contribute most to the model’s predictions?” Importantly: Feature importance does not change model performance It is used for interpretation , especially in clinical research Two main methods are used: Impurity-based importance (Gini importance) Permutation-based importance Method 1: Impurity-Based Importance (Gini Importance) Core Idea Each time a feature is used to split a node, it reduce

What is the Split Rule? At each node in a decision tree, the algorithm must decide: “Where should I split this feature to best separate the outcome?” This decision is governed by the split rule (criterion) . In Random Forest, the most common split rules are: Gini impurity (standard Random Forest) Extremely Randomized Trees (Extra Trees) The key difference lies in how the split threshold is chosen . The Core Difference: How a Split Point is Chosen Consider a single feature: Fe

Random Forest performance is driven by three core mechanisms: Tree strength (how well each tree fits the data) Tree diversity (how different trees are from each other) Ensemble averaging (how predictions stabilize across trees) Each parameter influences one or more of these mechanisms. Category 1: Tree Structure Parameters (Most Important for Performance) These parameters control how each individual tree grows and directly affect the bias–variance trade-off. 1. Features per s