Fate/strange Fake: A World That Should Not Exist Among all Fate spin-offs, Fate/strange Fake  occupies a singular and deliberately unstable position within the Nasuverse. It is not merely a parallel timeline or a divergent branch of Proper Human History, but a hybrid anomaly —a world where rules that are normally mutually exclusive coexist simultaneously. In simple terms: Fate/strange Fake is a “mixed world” where the laws of Fate-type universes and Tsukihime-type universes o

1️⃣ KEY DIAGNOSIS COMPARISON TABLE (VERY HIGH-YIELD) Feature Bursitis Synovial Cyst Baker’s Cyst (Popliteal Cyst) Definition Inflammation of a bursa Herniation of synovial lining Posterior knee synovial cyst Origin Bursa (extra-articular) Synovium Synovium Common location Prepatellar, infrapatellar, pes anserine Near joint line Popliteal fossa Joint communication ❌ No ✅ Yes ✅ Yes Compressible ± ✅ ✅ Changes with movement ❌ Minimal ± ✅ Size varies with knee movement Pain with w

💉 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)

Introduction MAPE (Mean Absolute Prediction Error) is often introduced as “average |prediction − truth|”, but in clinical prediction models (CPMs)  it deserves a bit more respect—because what counts as “truth”  depends on the outcome type, and because MAPE can look “good” even when a model is clinically misleading. Below is the deep, CPM-focused way to think about it. What MAPE really measures (conceptually) For a binary outcome (event vs no event): p̂ᵢ is the model’s predict

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