💉 Zoster Vaccine (Herpes Zoster / Shingles Vaccine) Overview Herpes zoster (shingles)  is caused by reactivation of varicella-zoster virus (VZV) , leading to a painful vesicular rash and potential complications such as postherpetic neuralgia (PHN) . Vaccination is the most effective method to prevent shingles and its complications. ⭐ Preferred Vaccine: Recombinant Zoster Vaccine (RZV) Brand name:   Shingrix® Shingrix is currently the vaccine of choice worldwide  for shingles

Dyshidrotic Eczema (Pompholyx) Spot diagnosis Intensely itchy Deep, clear vesicles Palms, sides of fingers, soles “Tapioca-like” appearance Triggers Stress, sweating Nickel/cobalt Irritants, atopy Diagnosis Clinical KOH only if tinea suspected Management (OPD) High-potency topical steroid Clobetasol 0.05% ointment BID × 7–14 days Emollients frequently Cold compress Severe flare Prednisone 0.5–1 mg/kg/day PO × 5–7 days (short course) Refractory Tacrolimus ointment Phototherapy

Introduction Sample size calculation is one of the most misunderstood aspects of medical research because there is no single universal rule. The correct approach depends entirely on what the study is trying to achieve. Designing a study is not merely about enrolling participants and running analyses. It is about anticipating the interplay between clinical importance, statistical rigor, ethical responsibility, and resource constraints. Sample size sits at the center of this ba

Sample size rules depend on the objective  of the research — there is no single universal rule. Introduction Sample size calculation is one of the most confusing parts of medical research because there is no single correct rule . The correct approach depends entirely on what the study is trying to achieve . Choose the Rule Based on Your Research Objective A study may aim to: test a hypothesis, build a prediction model, estimate a parameter precisely, evaluate a complex desig

Introduction Muscle cramps are sudden, involuntary, and painful contractions of skeletal muscles, most commonly affecting the calf muscles of the lower limbs. They are frequently encountered in clinical practice and are especially prevalent among athletes, pregnant women, elderly individuals, and patients with fluid or electrolyte disturbances. Understanding the physiological basis of muscle cramps is essential for accurate diagnosis, appropriate management, and success in me

Introduction Definition: Cor pulmonale is right ventricular (RV) dysfunction or failure secondary to pulmonary hypertension caused by diseases of the lungs, pulmonary vasculature, or chest wall  (❌ not due to left-sided heart disease). Clinical Presentation 1. Symptoms Early / Compensated stage Dyspnea on exertion (most common) Fatigue, reduced exercise tolerance Chest discomfort (due to RV ischemia) Palpitations Syncope or presyncope (suggests severe pulmonary hypertension)

This article integrates the TRIPOD+AI  reporting standards with the latest Riley/Collins/Ensor  stability framework. It shifts the focus from just "average" performance to "individual" reliability using the pm-suite  in Stata. Introduction In modern clinical prediction, showing that a model is "accurate on average" is no longer enough. Under TRIPOD+AI , you must report both Performance  (how well the model works for the population) and Stability  (how much an individual’s ris

Introduction Once you’ve shown discrimination, calibration, and clinical utility , a sophisticated reader asks a deeper question: “How stable are these predictions if the data were slightly different?” This is where many CPM papers stop too early. Two models can have identical AUROC, calibration, and DCA—yet one is fragile , overly dependent on idiosyncrasies of the development sample, while the other is robust . Stability analysis exposes this difference. In the CECS CPM fra

Fractional polynomials, selection control, and using dummy variables (xi: workaround) This article is focused only on mfp  (no MI, no validation workflow), and it is written for researchers who want to: model non-linear continuous predictors  in one regression model, and understand exactly what the key mfp syntaxes and options do, especially select() df() and the dummy-variable / xi: workaround  when factor-variable syntax is not accepted. 1) What problem does mfp solve? In m

A clinical-epidemiology article on what “df(1), df(2), df(4)” really mean (and what they do not  mean) Abstract Fractional polynomials (FP) are a structured approach for modeling non-linear associations between continuous predictors (e.g., age, hemoglobin, creatinine) and outcomes without categorizing variables or using unstable high-degree polynomials. In FP and Stata’s multivariable fractional polynomial (MFP) workflow, the degrees of freedom settings—linear (1 df), FP1 (2

Introduction This short “how-to” is written for researchers who hit the same wall you did: You want multivariable fractional polynomials (MFP)  for continuous predictors (non-linearity handling), but mfp does not accept factor-variable syntax  (i.var, c.var##c.var), and it often breaks inside mi estimate  unless you handle categorical variables correctly. The solution is usually simple: pre-create dummy variables  (best practice) or use xi:  (quick fix). mfpa is an alternativ

We don’t have to bootstrap before kNN because it’s not a rule—it’s just an optional way to reflect imputation variability, not internal validation. Bootstrap before kNN cannot be claimed as internal validation , because it does not involve model fitting and testing on different data and thus does not estimate optimism . Internal validation requires bootstrapping of the final model; bootstrapping the imputation step alone is insufficient, because we fit the model on the im

First: fix one wrong mental picture I am thinking: “If complete data can do bootstrap once,missing data must do bootstrap twice → this feels like cheating / overkill.” This feeling comes from counting datasets , but internal validation is not about counting datasets . 👉 Internal validation is about ONE comparison : Was the model evaluated on data it was NOT trained on? Everything else is bookkeeping. Case 1 COMPLETE DATA (no missing) ❓ What is the correct  internal validatio

(กรณี kNN imputation หลายชุด + Fractional Polynomial + Bootstrap) บทนำ: เรากำลังพยายามตอบคำถามอะไร? ในการพัฒนา prediction model  คำถามสำคัญไม่ใช่แค่ว่า “โมเดล fit กับข้อมูลเราได้ดีแค่ไหน?” แต่คือ “โมเดลนี้ overfit แค่ไหนและถ้าเอาไปใช้กับคนใหม่ประสิทธิภาพจะลดลงเท่าไร?” การตอบคำถามนี้เรียกว่า Internal Validation บริบทของงานนี้ (Your exact problem) งานนี้มีความซับซ้อน 3 ชั้นพร้อมกัน: Missing data จำนวนมาก → ใช้ Boostrap before kNN single imputation ซ้ำหลายครั้ง → ได้ imputed dat

Overview Missing data are common in clinical datasets and must be handled carefully to avoid biased estimates, inflated performance, and invalid inference. In this study, missing values were addressed using k-nearest neighbor (kNN) imputation  as an alternative to multiple imputation (MI) , followed by bootstrap-based internal validation  of a diagnostic prediction model for gastrointestinal malignancy. This section describes the theoretical justification , practical implemen

Etiological • Discovery • Predictive • Confirmatory (EDPC) Precision medicine papers often look  similar (omics + fancy plots), but they can be doing four very different jobs . Your slide deck defines these four objectives clearly: Etiological, Discovery, Predictive, Confirmatory . If you misclassify the objective, you will misread the results (e.g., treating “discovery” as “prediction”, or treating “prediction” as “clinical utility”). The EDPC map (what kind of paper is this