Find any research topic and learn it completely — for free. A structured curriculum covering everything from scientific writing fundamentals through advanced evidence synthesis.
Section 01
The conceptual foundations: what research is, how to read it, how to formulate it, and the ethical guardrails that frame everything that follows.
Defining scientific research, its purpose, and the role of medical research in evidence-based practice.
A high-level map of all major study designs and where each fits in the evidence pyramid.
Building tractable clinical questions using PICO and FINER frameworks.
How variables relate, why confounders matter, and how to spot effect modifiers.
Selection bias, information bias, and confounding — what they are and how to control for them.
From expert opinion to systematic reviews — how the hierarchy of evidence works.
A structured approach to extracting what matters from a paper in minutes, not hours.
Institutional Review Boards, informed consent, and the principles of responsible conduct.
Recognizing plagiarism, paraphrasing properly, and using similarity-detection tools.
Choosing between Zotero, EndNote, and Mendeley — and integrating them with your writing.
Registering systematic review protocols on PROSPERO and why pre-registration matters.
Section 02
Structure, tone, and craft. From a memorable title to a discussion that lands — the moves that make a manuscript publishable.
Introduction, Methods, Results, And Discussion — what each section should contain and what to leave out.
The anatomy of titles that get clicked, cited, and indexed well.
Structured vs unstructured abstracts: density, framing, and the moves that get a paper read.
Building a funnel from broad context to a sharp, novel research question.
Reproducibility, density, and the discipline of methods writing.
Reporting numbers, designing tables, and balancing prose with figures.
Interpreting findings, contextualizing within the literature, and acknowledging limitations honestly.
Saying what the work means without overreaching.
MeSH, indexing, and the keywords that surface your work in the right searches.
The recurring errors reviewers flag — and how to avoid them.
Matching the right reporting standard to your study design.
Section 03
Each design has its own conventions. Tailoring the manuscript to systematic, network, or narrative-review formats.
From protocol to PRISMA flow to forest plots — building a publication-grade SRMA.
Indirect comparisons, transitivity, league tables, and SUCRA-based ranking.
Synthesizing a field without a protocol — when narrative reviews are the right tool.
Section 04
Deep dives: when each design applies, how it works, what biases threaten it, and how to read it critically.
Randomization, blinding, allocation concealment, intention-to-treat — the gold standard, dissected.
Cohort, case-control, cross-sectional — strengths, limitations, and when to choose each.
Case reports, case series, ecological studies — what they tell us and what they cannot.
Systematic, scoping, narrative, and umbrella reviews — choosing the right format.
Section 05
From descriptive statistics to network meta-analysis. The full statistical toolkit for evidence-based research, in order.
Nominal, ordinal, interval, ratio — and why the type drives the test.
Mean, median, standard deviation, IQR — choosing the right summary for your data.
Histograms, boxplots, scatterplots — how to make figures that tell the truth.
The ideas behind every statistical test — distributions, expectation, and variance.
What CIs and p-values actually mean — and what they don't.
Why a p-value of 0.001 may not matter, and a non-significant trend may.
t-tests, chi-square, ANOVA, Mann-Whitney, Wilcoxon — picking the right comparison.
Linear and logistic regression: building, interpreting, and reporting models.
Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression.
Effect sizes, weighting, pooling — the math behind a forest plot.
When to use each, and what the choice implies about heterogeneity.
I², τ², Q-statistic — quantifying and interpreting between-study variation.
Funnel plots, Egger's test, trim-and-fill — detecting and adjusting for missing studies.
Exploring sources of heterogeneity through covariate-adjusted meta-regression.
League tables, SUCRA, inconsistency testing, and node-splitting.
Getting started with R for clinical and meta-analytic work.
Cochrane's tool for systematic reviews — from data entry to forest plot.
Practical SPSS for clinical research, walked through end-to-end.
From messy spreadsheet to analysis-ready dataset — the prep work that gets ignored.
Section 06
From manuscript-finished to paper-published. Journal selection, submission, reviewer responses, and the politics of authorship.
Aim, scope, indexing, audience — matching paper to journal in 30 minutes.
What IF really measures, alternatives like CiteScore, and why indexing matters.
The cover letter as elevator pitch — what to include, what to skip.
Walking through manuscript submission on a major publisher's portal.
Tone, structure, and the art of conceding gracefully — and pushing back when warranted.
Re-reading, re-targeting, and turning a rejection into a stronger next submission.
Who deserves to be an author — and the politics of getting it right.
Spotting predatory publishers and protecting your CV.
APCs, licenses, embargoes — and what each model costs in money and reach.
PPTX
Downloadable companion slides for each lecture. Use them to teach, study, or build on for your own work.
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