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IMF-066-01-0001-en	Why is evidence-based medicine not the same as obeying an evidence hierarchy?	It integrates the best research with clinical expertise, patient values, biology, resources, and context, because research only estimates effects under uncertainty and clinicians must judge validity, importance, applicability, and fit with the patient's goals.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0002-en	Why does good interpretation examine methods before results, and what distinction must it keep?	Methods decide whether results can be trusted at all, and absence of evidence must be distinguished from evidence of no effect.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0003-en	How does the structure of a focused question differ between treatment and diagnosis, and why does vagueness matter?	Treatment questions define population, intervention, comparator, and outcomes; diagnostic questions define target condition, test, reference standard, and setting. A vague question encourages selective evidence and irrelevant endpoints.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0004-en	Why can biomarkers and composite outcomes mislead despite improving statistical efficiency?	They may be only weakly linked to patient benefit, or a composite may be driven by its less important components.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0005-en	What evidence is required to validate a surrogate outcome?	Evidence that changing the surrogate reliably changes the clinical outcome across the relevant mechanisms, not merely that the two are associated.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0006-en	How do randomisation, concealed allocation, and blinding each protect a trial?	Randomisation balances known and unknown prognostic factors on average, concealment prevents prediction of assignment before enrolment, and blinding reduces differential care, reporting, and assessment.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0007-en	What distinguishes intention-to-treat from per-protocol analysis in what each estimates and risks?	Intention-to-treat analyses assigned groups, preserving randomisation and estimating a treatment strategy under real adherence; per-protocol estimates effects in adherent participants but loses randomisation and is vulnerable to selection.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0008-en	Why must cluster trials account for within-cluster similarity, and when do crossover trials suit?	Cluster trials randomise groups whose members resemble each other, so this similarity must be modelled; crossover trials suit stable conditions without carryover.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0009-en	Why are case-control studies efficient for rare disease, and what makes them vulnerable?	They begin with the outcome and compare prior exposure, so rare cases need not be awaited, but this design is vulnerable to selection and recall.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0010-en	Why can meta-analysis precisely estimate the wrong thing?	Combining studies increases precision but does not correct biased inputs, and pooling incomparable studies yields a precise estimate of the wrong construct.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0011-en	What does a ninety-five per cent confidence interval mean under repeated sampling, and what common reading is only shorthand?	Across repeated identical studies, ninety-five per cent of such intervals would contain the true parameter; it is not strictly the probability that this one interval contains the truth.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0012-en	What does a p value not tell you, and why does statistical significance depend on study size?	It is not the probability the null is true, that results arose by chance, or the size and importance of an effect; large studies detect trivial differences while small studies can miss important ones.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0013-en	Why is the number needed to treat not a fixed property of a drug?	It varies with the baseline risk of the population and the length of follow-up.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0014-en	Why do non-inferiority and equivalence trials need prespecified margins, adequate adherence, and careful analysis?	Non-significance does not establish equivalence, and bias toward no difference can falsely support a claim of success.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0015-en	How do selection, performance, detection, and attrition bias differ in where they arise?	Selection bias comes from inclusion or retention creating prognostically different groups, performance bias from unequal co-interventions or attention, detection bias from differing outcome measurement, and attrition bias from missing outcomes related to treatment and prognosis.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0016-en	How does random measurement error differ from systematic error in its effect on an estimate?	Random error reduces precision, whereas systematic error shifts the estimate in one direction.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0017-en	How does confounding create a non-causal association, and why can adjustment itself introduce bias?	A third factor predicts exposure and independently affects outcome; adjusting for a variable caused by exposure, a mediator, or a collider can create bias, and unmeasured confounding is never guaranteed removed.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0018-en	Why does triangulation across study designs strengthen causal inference when trials are not possible?	Designs with different biases are unlikely to be misled in the same way, so agreement among them is more convincing than any single design.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0019-en	How do reverse causation and immortal-time bias distort apparent exposure effects?	In reverse causation, early disease changes the apparent risk factor; immortal-time bias grants exposed patients a period in which the outcome could not occur, flattering exposure.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0020-en	Why do sensitivity and specificity vary between settings while predictive values vary with prevalence?	Sensitivity and specificity depend on disease spectrum and thresholds, whereas predictive values depend on how common the condition is in the tested population.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0021-en	What does the area under a receiver-operating-characteristic curve fail to measure, and why can a discriminating test add little?	It measures discrimination but not calibration, utility, or outcomes, and a discriminating test adds little if management does not change.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0022-en	Why is a hazard ratio not a risk ratio, and why does ignoring competing events overestimate incidence?	A hazard ratio compares instantaneous rates under proportional hazards; competing events such as death before fracture prevent the outcome, so ignoring them inflates its estimated incidence.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0023-en	Why can spontaneous reports detect drug-safety signals but not determine incidence, and what is channeling bias?	Reporting and the denominator are unknown, so frequency cannot be calculated; channeling bias arises when high-risk patients preferentially receive one treatment.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending
IMF-066-01-0024-en	Why does significance in one subgroup and non-significance in another not prove a difference, and how is certainty distinct from benefit?	Only a statistically tested interaction supports a difference, and data-driven subgroups often yield chance findings; high certainty does not mean large benefit, nor low certainty no benefit.	Integrated Medical Foundations; chapter 66, TTS module 1; https://theisegoria.github.io/medical-textbook/integrated-medical-foundations-expanded-english.md; module SHA256 at export: 2e04606571cd0fe096b5f623d0ef844b94b61a0927a5410afbb3d56041d7d2b7; editorial review pending	imf lang::en chapter::066 module::066-01 review::pending