Pediatric Dosage Calculator - 10 Formulae

Pediatric Dosage Calculator

Adult Dose (mg)

Age (Years)

Age (Months - Fried's)

Weight (kg)

Weight (lbs)

Height (cm - BSA)

Select Formula to Visualize Young's Rule Dilling's Rule Bastedo's Rule Fried's Rule Cowling's Rule Webster's Rule Clark's Rule (kg) Clark's Rule (lbs) Modified Weight Rule Majid's Rule BSA-Based Formula

Rule / Formula Name	Calculated Dose

Visualizing: Young's Rule

Learning The Recognition-Primed Decision (RPD) model of Decision-Making

Mastering the RPD Model

The Recognition-Primed Decision (RPD) model explains how experts make split-second choices under pressure by recognizing familiar patterns rather than comparing lists of options.

1. The Decision Cycle

The loop of Experiencing, Analyzing, and Implementing.

2. Match the Concept

Drag the descriptors into the correct stage of the RPD Cycle:

Gathering Cues

Mental Simulation

Taking Action

Pattern Matching

Sensory Input

Satisficing

Experiencing

Analyzing

Implementing

3. Reflection & Discussion

The Psychology of Choice

A deep-dive into Prospect Theory and why we fear loss more than we love gain.

Prospect Theory

Developed by Kahneman & Tversky, this Nobel-winning theory explains that humans value gains and losses differently. We don't think in absolute wealth, but in changes from our current state.

Risk Aversion

When looking at potential gains, we get "scared." We prefer a bird in the hand. Faced with a sure gain, most people choose the safe bet over a gamble for more.

Loss Aversion

Pain from a $100 loss is twice as intense as the joy of a $100 gain. This "asymmetry" drives almost all human economic behavior.

Risk Seeking

When faced with a sure loss, our brains flip. We become "gamblers," willing to take huge risks just for a small chance to break even and avoid the sting of losing.

Status Quo Bias

We are wired to stay put. We treat the current situation as a reference point, and any change from it is perceived as a potential loss.

1. Interactive Scenario Lab

Where do these human tendencies belong? Drag each scenario into the correct psychological bucket.

SAFE & STEADY (Averse)

GAMBLE IT ALL (Seeking)

Sure gain of $500

Sure loss of $500

"90% Survival Rate"

"10% Mortality Rate"

Keeping the default 401k

2. Challenge Your Thinking

Advanced Exploration

Q1: Why do people continue to watch a boring movie just because they paid for the ticket?

Sunk Cost Fallacy: We treat the spent money as a "loss" that we are trying to "recover" by staying. Rationally, the money is gone regardless; staying only loses you time.

Q2: Why are we more likely to buy a $50 shirt marked down from $100 than a $50 shirt with no discount?

Anchoring: The $100 price becomes the reference point. Buying it at $50 feels like a $50 "gain" rather than a $50 expense.

Q3: Why do people buy extended warranties for small electronics when the math rarely favors the consumer?

Probability Weighting: We over-weight small probabilities of "disaster" (the device breaking). We pay a premium to eliminate the possibility of the "pain of loss."

Q4: Why is it harder to quit a job you hate than to turn down a job offer you don't like?

Status Quo Bias: Quitting is an active choice that feels like a "loss" of security, whereas turning down a new offer is maintaining the current state.

Q5: How does "Narrow Framing" lead to bad financial decisions?

Narrow Framing: This happens when we look at every investment individually. If one stock drops, we feel the "loss" intensely and sell, even if our overall portfolio is up significantly.

Q6: Why do "Trial Periods" (e.g., Netflix for 30 days) work so well for companies?

Pseudo-Endowment: Even before you pay, you begin to incorporate the service into your life. Ending the trial feels like losing a possession rather than just not buying a service.

Q7: Why do people prefer a 100% chance of winning $450 over a 50% chance of winning $1,000?

The Certainty Effect: We over-value outcomes that are certain. Even though the "expected value" of the gamble is higher ($500), the psychological safety of "sure money" wins.

Q8: In a crisis, why do leaders often take huge risks that make things worse?

Risk Seeking in Losses: When leaders feel they are in a "losing" position, they often gamble on high-risk strategies to get back to the status quo rather than accepting a smaller, certain loss.

Q9: Why do we feel better when we lose a $20 bill we found on the street vs. a $20 bill we earned at work?

Mental Accounting: We categorize money based on its source. "Found" money is often put in a "play" bucket, so its loss doesn't hit our "hard-earned" reference point as hard.

Q10: How does "Diminishing Sensitivity" affect our joy of winning?

The difference between winning $0 and $100 feels huge. The difference between winning $1,000 and $1,100 feels much smaller. Our emotional response flattens as the amounts grow.

Bayes' Theorem - Educational Content

Bayes' Theorem Visualizer

Understand how to revise predictions based on new data.

The Definition

Bayes' Theorem is a fundamental formula in probability that calculates the updated probability of an event (A) given new evidence (B). It relates the likelihood of the evidence to the prior belief.

P(A|B) =

P(B|A) ⋅ P(A) P(B)

Where:
P(A|B) The updated probability after seeing the evidence. Also called the result. is the Posterior,
P(B|A) The probability of seeing this evidence IF the hypothesis is true. is the Likelihood,
P(A) Your initial belief before seeing any new evidence. is the Prior, and
P(B) The total probability of the evidence occurring, regardless of whether the hypothesis is true or false. is the Evidence.

 Interactive Practice

Memorize the Formula

Try writing the theorem below to commit it to memory.

P(A|B) = [P(B|A) * P(A)] / P(B)

Bayesian Probability Interactive

Adjust sliders to see how evidence updates belief.

TP

FP

Final Probability P(H|E)

16.7%

Given a positive result, this is the chance it's actually true.

P(H) Prior 1% The "Prior" width: How common is the condition?

P(E|H) Sensitivity 99% The height of the Dark Blue box.

P(E|¬H) False Pos 5% The height of the Red box.

Animated Graph maker using R-Project-Shiny-based Packages

This animated graph maker has been developed using R-Project (4.4.1), RStudio 2025.09.1 Build 401. It is made using packages, including shiny, ggplot2, gganimate, gifski, av, dplyr, readr, shinycssloaders, gapminder, rlang, and shinyjs..

Check my other online advanced statistical tools here:

1. Structural Equation Modeling using Lavaan package in R - https://jeepakistan.blogspot.com/2025/07/structural-equation-modeling-sem-using.html

2. Meta-analysis using Odds Ratio (OR) in Microsoft Excel - https://jeepakistan.blogspot.com/2025/06/meta-analysis-using-odds-ratio-or-in.html

3. Meta-analysis using Cohen's d in Microsoft Excel - https://jeepakistan.blogspot.com/2025/05/meta-analysis-using-cohens-d-in.html

4. Meta-analysis using Correlation (r) in Microsoft Excel - https://jeepakistan.blogspot.com/2025/05/meta-analysis-using-correlation-r-in.html

5. Meta-Analysis using R-Project-based Metafor, and Online calculator (R-Project-Shiny-based) - https://jeepakistan.blogspot.com/2025/09/meta-analysis-using-r-project-based.html

6. Time Series Analyses using R-Project-based Packages, and Online calculator (R-Project-Shiny-based) - https://jeepakistan.blogspot.com/2025/10/time-series-analyses-using-r-project.html

 Welcome to this Physiology & Pharmacology Receptor Memory Game...

This game has been developed to help students memorize over 100 physiological and pharmacological receptors along with their related drugs (agonists or antagonists etc.).

In this game, you have to flip cards on the screen to match each receptor with its correct ligand or drug helping you in memorizing them through spaced repetition and adaptive learning.

You may either "Play All" which is the standard path to help you learn receptors and related drugs without getting overwhelmed, or you can select a specific category of receptors and memorize its receptors and related drugs. 

It has two phases to help you learn. Phase 1 relates to your exposure to only two pairs, and after completing Phase 1, you will go to Phase 2, where all pairs will appear. 

You may also select Smart Review mode in which only 8 pairs will appear that you may found difficult during your play. Your progress is saved in your computer's local storage, and this Smart Review will work on the Spaced Repetition Algorithm and brings forward only difficult pairs that you may found difficult to learn.

Note: Your feedback is important to eliminate/reduce any errors or mistakes in this game.  

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Physiology & Pharmacology Receptor Memory Game

Category: Play Smart Review

Loading progress...

Click on the cards to match receptor with its ligand/drug.

Time Series Analyses using R-Project-based Packages, and Online calculator (R-Project-Shiny-based)

This online calculator has been developed using R-Project (4.4.1), RStudio 2025.09.1 Build 401. It is made using packages, including shiny, shinydashboard, forecast, xts, rugarch, vars, tseries, ggplot2, plotly, zoo, tidyr, DT, shinycssloaders, and dplyr..

Check my other online advanced statistical tools here:

1. Structural Equation Modeling using Lavaan package in R - https://jeepakistan.blogspot.com/2025/07/structural-equation-modeling-sem-using.html

2. Meta-analysis using Odds Ratio (OR) in Microsoft Excel - https://jeepakistan.blogspot.com/2025/06/meta-analysis-using-odds-ratio-or-in.html

3. Meta-analysis using Cohen's d in Microsoft Excel - https://jeepakistan.blogspot.com/2025/05/meta-analysis-using-cohens-d-in.html

4. Meta-analysis using Correlation (r) in Microsoft Excel - https://jeepakistan.blogspot.com/2025/05/meta-analysis-using-correlation-r-in.html

5. Meta-Analysis using R-Project-based Metafor, and Online calculator (R-Project-Shiny-based) - https://jeepakistan.blogspot.com/2025/09/meta-analysis-using-r-project-based.html