1. Effect Size: The Foundation of Clinical Interpretation Effect size (ES) is the magnitude of difference or association between groups, exposures, treatments, or predictors. It is the central component of all DEPTh areas (diagnosis, etiology, prognosis, therapeutic, methodologic). “Always interpret effect size + 95% CI, not p-values alone.” Common Effect Size Metrics by Research Type DEPTh Type Effect Size Metrics Therapeutic Risk Ratio, Risk Difference, Mean Difference, H

1. Definition A muscle cramp is a sudden, painful, involuntary contraction of a muscle or muscle group, most commonly affecting the calves , feet , hamstrings , and sometimes hands or abdomen. Most cases are benign and arise from neuromuscular irritability due to electrolyte imbalance, dehydration, or muscle fatigue. 2. Epidemiology Very common: affects up to 60% of adults . More frequent at night and in elderly. Certain populations (pregnant women, athletes, diuretic users

1. Overview: Markdown vs Quarto Markdown (MD) Markdown is a lightweight markup language used to format text into headings, bold/italic, lists, tables, code blocks, etc. Strengths: Simple syntax Ideal for README files, notes, tutorials Supported anywhere: GitHub, VS Code, RStudio, Stata logs (via dyndoc) Good for static documentation Limitations: Limited automation No native support for executing code chunks Cannot render statistical results automatically → Markdown alone is N

1. How they present external validation (text + tables) 1.1 Paper A – IDIOM model with updating Structure of the text Introduction Clinical background: iron deficiency anemia, GI malignancy. Introduces IDIOM model, original performance. States two aims : Describe prevalence/clinical characteristics of GI malignancy in Thai IDA patients Externally validate IDIOM and update it if needed. Methods Study design & patients (retrospective, single center, inclusion criteria, defini

1. Definition Detrusor underactivity (DU) or Underactive Bladder (UAB) is a condition where the bladder muscle (detrusor) contracts too weakly and/or too briefly to empty the bladder completely during voiding. Formal urodynamic definition (ICS – International Continence Society): “A contraction of reduced strength and/or duration, resulting in prolonged bladder emptying and/or failure to achieve complete bladder emptying within a normal time span.” Clinically, you will see:

1. What is seborrheic dermatitis? Seborrheic dermatitis is a chronic, relapsing inflammatory skin disease affecting sebaceous (oil-rich) areas : Scalp Face (especially eyebrows, nasolabial folds, glabella) Ears, presternal chest, upper back, body folds It’s strongly associated with Malassezia (yeast) , sebum, and abnormal immune response. Prevalence: about 1–3% of the general population , much higher in HIV and neurologic disease (e.g. Parkinson’s). 2. Pathophysiology (high-

1. What Is IBS? Irritable Bowel Syndrome (IBS) is a chronic functional gastrointestinal disorder characterized by recurrent abdominal pain associated with changes in stool frequency and/or form , without structural or biochemical abnormalities on routine testing. In simple terms: The gut looks normal (on colonoscopy, imaging) but functions abnormally. 2. Epidemiology & Impact Prevalence: ~5–10% of the population worldwide, depending on criteria used. More common in: Fema

Beyond Calibration: The Rise of Prediction Stability While calibration corrects the long-standing misconception that discrimination alone is sufficient, it does not answer a deeper structural question: “Can I trust the model to give the same predictions if the training sample had been slightly different?” This is the domain of Prediction Stability —a newly recognized, methodologically essential property of Clinical Prediction Models (CPMs), highlighted in modern CPM developme

How to measure agreement beyond chance 1. Why do we need Kappa? When two raters (or two methods) classify patients into categories—for example: Fracture: yes / no CT severity: mild / moderate / severe ECG finding: normal / abnormal we want to know: Do they really agree, or are they just “lucky” to match by chance? Simple percent agreement (% of cases where both give the same category) is easy to understand but has a big limitation: If one category is very common (e.g., “no d

1. Where does a clinical score come from? Most modern scores come from a prediction model , usually: Logistic regression for binary outcomes (e.g. appendicitis: yes/no) Cox model for time-to-event (e.g. 10-year CVD risk) For a logistic model, the development team fits: Y = outcome (e.g. disease yes/no) Xj = predictors (e.g. fever, RLQ pain, WBC, etc.) α = intercept βj = log-odds coefficients Those βj are the true origin of the score. The score is just a simplified, rounded v

1. Why do we care about reliability? When you design a clinical tool (score, scale, questionnaire, diagnostic classification), you usually have: Raters (people or systems making the judgment) Repeated measurements (same patient, same test, measured twice or more) Categorical outcomes (e.g. “present/absent”, “stage I/II/III”, “mild/moderate/severe”) You want to know: Agreement – Do raters give the same category? Reliability – Does the measurement reflect true differences

Step 0 – Load the external validation cohort clear cd "C:\WORK\Location\External validation" use "validation.dta", clear summarize Why? We start with the validation dataset only . Step 2 is about how the original model behaves in a new population – no re-fitting from scratch, just evaluation and recalibration. Step 1 – Reconstruct the original linear predictor and risk * 1.1 Generate the linear predictor (log-odds) using original coefficients gen logodds = -4.4415 /// + 0.

Steps to Developing a Clinical Prediction Model (CPM) Choose predictors & run forward/backward Fit the final logistic model cleanly Read α (intercept) and β’s from Stata Write the prediction equation Generate LP and risk in Stata All in Stata-only . Step 1 – Start with development data and candidate predictors clear use "your_development_data.dta", clear * Inspect variables describe summarize Assume you want to predict death (0/1) from a set of candidate predictors: age sex

Abstract Calibration is a fundamental property of clinical prediction models (CPMs), reflecting how well predicted probabilities agree with actual observed outcomes. Unlike discrimination—how well a model distinguishes between individuals with and without an event—calibration evaluates absolute accuracy. Poor calibration can mislead clinical decision-making even when discrimination appears acceptable. This article explains the conceptual foundation, metrics, and practical int

INTRODUCTION As clinicians, we constantly face questions such as “Does this treatment really work?” or “Should I trust this new study?” ในฐานะแพทย์ เรามักเจอคำถามว่า “วิธีรักษานี้ได้ผลจริงไหม?” หรือ “งานวิจัยใหม่นี้เชื่อได้แค่ไหน?” Being able to read and interpret RCTs is essential because RCTs are the gold standard for therapeutic evidence and commonly appear in licensing exams. ทักษะการอ่านและแปลความหมาย RCT เป็นสิ่งจำเป็น เพราะ RCT เป็นหลักฐานสูงสุดด้านการรักษา และออกสอบใบ

Introduction As clinicians, we constantly face questions such as “Is this drug effective?” , “Is that treatment truly better?” , or “This new study says it works — should we believe it?” These questions come from patients, colleagues, hospital administrators, and even from within our own decision-making as we choose the best treatment for the person in front of us. Because of this, one of the most important skills for every physician is the ability to read, interpret, and jud

1. Diagnosis Criteria 🔵 A. Nail Psoriasis – Diagnostic Criteria Clinical Diagnosis (no single gold standard). Diagnosis is based on classic nail findings + history of psoriasis . Major Nail Features Pitting Oil-drop (salmon patch) discoloration Onycholysis with erythematous border Subungual hyperkeratosis (psoriatic type: chalky, white) Nail crumbling / roughness Leukonychia Supportive Features Current or past cutaneous psoriasis Psoriatic arthritis Family history of psorias

✅ 1. Diagnosis of Acute Otitis Media (AOM) Diagnostic Criteria — must have ALL: A. Acute symptoms Fever Otalgia (ear pain) Irritability in children Otorrhea (only if TM perforation) B. Middle-ear inflammation Seen on otoscopy: Bulging tympanic membrane (TM) — most specific finding Erythema of TM Reduced mobility on pneumatic otoscopy C. Middle-ear effusion Opaque TM Air-fluid level Loss of TM landmarks 👉 Bulging TM + acute ear pain = AOM until proven otherwise. ✅ 2. Diagn

✅ 1. Amoxicillin Regimen (Ready to Use) Amoxicillin (500 mg) 2×3 po pc for 7 days ✔ Meaning: 500 mg tablets, take 2 tablets (1 g) three times a day , after meals , for 7 days . Final Prescription Line: Amoxicillin (500 mg) 2×3 po pc × 7 days ✅ 2. Cefdinir Regimen (Ready to Use) Two common OPD pneumonia dosing patterns exist. Use whichever your professor prefers. Option A: Standard Adult CAP Regimen Cefdinir (300 mg) 1×2 po bid × 7 days ✔ Meaning: 300 mg one capsule , twice a

Introduction The choice between Fixed-effects , Random-effects , and Mixed-effects models fundamentally shapes how clinicians and researchers interpret pooled evidence. In therapeutic evaluation, causal inference, and complex trial designs, the model you choose determines whether your conclusions reflect a single underlying effect , an average effect across diverse settings , or a heterogeneity-explained effect dependent on study-level characteristics . Grounding this logic