How the USPSTF Built Its Adult Depression Screening Recommendation
A plain-language walk through the USPSTF Grade B for adult depression screening, the evidence links behind it, and why follow-up systems count.
Health & Evidence Library
Tools for reading studies, interpreting tests and effect estimates, recognizing uncertainty, and asking whether evidence applies.
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A plain-language walk through the USPSTF Grade B for adult depression screening, the evidence links behind it, and why follow-up systems count.
A reader's guide to preregistration and Registered Reports, and how the order of plan versus results helps you judge a study's claims.
How GRADE rates certainty of evidence and uses the Evidence-to-Decision framework to reach strong or conditional guideline recommendations.
Why stable chest pain leads to CT angiography, stress imaging, or no test at all, based on the estimated likelihood of coronary disease.
How depression rating scales like the Hamilton and MADRS are scored, what a clinically important difference means, and why their cutoffs are debated.
How Wells, PERC, age-adjusted D-dimer, and YEARS rule out pulmonary embolism safely, and the failure-rate evidence behind skipping the scan
Guidelines change when a panel decides the advice no longer fits the evidence. How to read an update by its date, its trigger, and its dissent.
How specialty societies build Choosing Wisely lists, the three tests each item must pass, and why the evidence behind them varies enough to deserve appraisal.
Why basket trials group cancers by shared mutation, how NTRK-fusion drugs earned tumor-agnostic approval, and what single-arm evidence cannot settle
Why a trial commits to one primary endpoint, the trade-offs between hard outcomes and surrogates, and what the choice reveals to a reader.
A plain-language guide to adaptive randomization, arm-dropping, and platform trials, and the efficiency they buy against the discipline they demand.
How the Revised Cardiac Risk Index was built and tested, and what discrimination, calibration, and prediction versus causation actually mean.
A plain-language guide to how GRADE rates evidence certainty across five domains, and why a strong recommendation and strong evidence are not the same thing.
A plain-language guide to single-arm and externally controlled trials, when regulators may accept them, and how comparability and timing can distort the result.
Efficacy is whether a treatment works under ideal conditions. Effectiveness is whether it works in everyday care. Check which one a claim means.
An evidence appraisal of the dodo bird debate and comparative meta-analyses on whether CBT outperforms other talk therapies
Randomized trials show advance care planning rarely makes end-of-life care match patients' wishes, a lesson in trusting countable proxies.
Association is not causation. A confounder is a third factor tied to both cause and effect, and study design exists to rule it out.
What a composite endpoint is, why trials use them, and how to read one without being misled by an impressive combined number.
How basket, umbrella, and platform trials differ, and how master protocols lead to tissue-agnostic FDA approvals.
A practical way to check a health news story against the research behind it, so you can tell solid reporting from overstatement.
Why large national health registries are a research powerhouse, what makes their findings strong, and where their limits lie.
A careful look at what observational studies on statins and cancer can and cannot tell us, and why this topic is a good lesson in reading evidence well.
Why two things moving together does not mean one causes the other, what confounding is, and how to think clearly about observational findings.
A friendly guide to the main kinds of medical studies, what each is good for, and why some sit higher than others when judging cause.
Why a claim that a treatment cuts risk by 50 percent can mean a lot or almost nothing, and how to tell which, in plain terms.
How to read a medical study without a statistics degree. A calm, six-step checklist for absolute risk, confidence intervals, p-values, and confounding.