When a sign, test or symptom has an extremely high specificity (say, over 95%), a positive result tends to rule in the diagnosis. For example, the specificity of 3 or more positive responses on a CAGE questionnaire in diagnosing alcoholism is >99% among internal medicine patients. Therefore, if a person does answer “yes” to 3 or 4 of the CAGE questions, it rules in the diagnosis of alcohol dependency.
When a sign, test or symptom has a high sensitivity, a negative result rules out the diagnosis. For example, the sensitivity of the loss of retinal vein pulsation in diagnosing high intracranial pressure is 100 per cent. Therefore, if a person displays retinal vein pulsation, it rules out important increases in intracranial pressure.
Source: CEBM https://www.cebm.net/
The Likelihood Ratio (LR) is the likelihood that a given test result would be expected in a patient with the target disorder compared to the likelihood that that same result would be expected in a patient without the target disorder. For example, you hav e a patient with anaemia and a serum ferritin of 60mmol/l and you find in an article that 90 per cent of patients with iron deficiency anaemia have serum ferritins in the same range as your patient (= sensitivity) and that 15 per cent of patients with oth er causes for anaemia have serum ferritins in the same range as your patient (1 – specificity). This means that your patient’s result would be six times as likely (90/15) to be seen in someone with, as opposed to someone without, iron deficiency anaemia, and this is called the LR for a positive test result.
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The Number Needed to Treat (NNT) is the number of patients you need to treat to prevent one additional bad outcome (death, stroke, etc.). For example, if a drug has an NNT of 5, it means you have to treat 5 people with the drug to prevent one additional bad outcome. More detailed discussion of the nature of the NNT measure can be found in the EBM Note on summarising the effects of therapy in the journal Evidence-Based Medicine 1997;2:103-4.
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Pretest Probability is defined as the probability of a patient having the target disorder before a diagnostic test result is known. It represents the probability that a specific patient, say a middle-aged man, with a specific past history, say hypertension and cigarette smoking, who presents to a specific clinical setting, like Accident and Emergency, with a specific symptom complex, say retrosternal chest pressure, dyspnoea and diaphoresis, has a specific diagnosis, such as acute myocardial infarction.
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