{"id":4004,"date":"2026-09-10T11:22:54","date_gmt":"2026-09-10T09:22:54","guid":{"rendered":"https:\/\/science-x.net\/?p=4004"},"modified":"2026-09-10T11:22:57","modified_gmt":"2026-09-10T09:22:57","slug":"bayes-theorem-in-medicine-why-a-positive-test-for-a-rare-disease-is-not-always-a-diagnosis","status":"publish","type":"post","link":"https:\/\/science-x.net\/?p=4004","title":{"rendered":"Bayes\u2019 Theorem in Medicine: Why a Positive Test for a Rare Disease Is Not Always a Diagnosis"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">A positive medical test can be frightening. Many people naturally assume that if a test comes back positive, the disease is almost certainly present.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">But that is not always true.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The meaning of a positive result depends not only on how accurate the test is, but also on <strong>how common the disease was before testing<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is where Bayes\u2019 theorem becomes extremely important.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bayesian reasoning helps doctors interpret test results in context. It explains why even a highly accurate test can produce a surprising number of false positives when screening for a very rare condition.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>A positive test changes the probability of disease. It does not automatically make that probability 100%.<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What Is Bayes\u2019 Theorem?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Bayes\u2019 theorem is a mathematical rule for updating probability when new information becomes available.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In medicine, it helps answer a practical question:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">How likely is a patient to actually have a disease after receiving a particular test result?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The answer depends on two broad things.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">First, how likely the disease was before the test.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Second, how well the test distinguishes people with the disease from people without it.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This means the same positive result can have very different meanings in different patients.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The Importance of Pre-Test Probability<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Before a test is performed, doctors already have some estimate of how likely a disease is.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is called the <strong>pre-test probability<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It may be influenced by:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Age<\/li>\n\n\n\n<li>Symptoms<\/li>\n\n\n\n<li>Family history<\/li>\n\n\n\n<li>Exposure history<\/li>\n\n\n\n<li>Other medical conditions<\/li>\n\n\n\n<li>Physical examination<\/li>\n\n\n\n<li>How common the disease is in the population<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">If a disease is extremely rare and a healthy person has no symptoms or risk factors, the pre-test probability may be very low.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If another person has classic symptoms and known risk factors, the probability may already be much higher.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Bayes\u2019 theorem combines this starting probability with the test result.<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Sensitivity and Specificity<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Two important measures describe medical test performance.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Sensitivity<\/strong> is the proportion of people with the disease who receive a positive result.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A highly sensitive test misses relatively few true cases.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Specificity<\/strong> is the proportion of people without the disease who receive a negative result.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A highly specific test produces relatively few false positives.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These measures are important, but they do not directly answer the question most patients care about:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cIf my test is positive, what is the chance I actually have the disease?\u201d<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That question requires information about disease prevalence or pre-test probability.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">A Simple Example<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Imagine a rare disease affecting 1 in 1,000 people.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Now imagine a screening test with:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>99% sensitivity<\/li>\n\n\n\n<li>99% specificity<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Those numbers sound excellent.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Suppose 100,000 people are tested.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">About 100 people would actually have the disease.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">With 99% sensitivity, approximately 99 of them would test positive.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Now consider the 99,900 people who do not have the disease.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">With 99% specificity, about 1% would falsely test positive.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That means roughly 999 healthy people could receive a positive result.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">So among all positive results, there would be approximately:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>99 true positives<\/li>\n\n\n\n<li>999 false positives<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">A positive result would therefore not mean a 99% chance of disease.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The actual probability would be only around <strong>9%<\/strong> in this simplified example.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That is the power of the base rate.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Why This Feels So Counterintuitive<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">People often focus on the test\u2019s accuracy and ignore how rare the disease is.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If a test is described as \u201c99% accurate,\u201d it is tempting to assume that a positive result means a 99% chance of illness.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">But that interpretation is usually incorrect.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The test is being applied to a population containing many more healthy people than sick people.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Even a small false-positive rate can produce a large number of false alarms when millions of healthy people are tested.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Rare diseases create a mathematical environment where false positives can outnumber true positives.<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">What Is a False Positive?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A false positive occurs when a test indicates that a disease or condition is present when it actually is not.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">False positives can happen for many reasons.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Biological measurements naturally vary.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Laboratory instruments are not perfect.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Other conditions may produce similar markers.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Cutoff values can classify borderline results as positive.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">No medical test is perfect in every patient.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A false positive does not necessarily mean the test is poor. It may simply reflect the unavoidable trade-offs involved in detecting disease.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Why Screening Programs Are Especially Affected<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Bayesian reasoning is particularly important in population screening.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Screening means testing people who may have no symptoms.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Because many screened individuals are healthy, the starting probability of disease can be low.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is why screening programs are carefully designed.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Doctors and public-health specialists consider whether early detection improves outcomes, how accurate the test is, what confirmatory tests are available, and whether unnecessary investigations could cause harm.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>A screening test is often the beginning of diagnosis, not the final step.<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Positive Predictive Value<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The probability that a person truly has the condition after receiving a positive test is often described as the <strong>positive predictive value<\/strong>, or PPV.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">PPV depends strongly on prevalence.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When a disease becomes more common in the tested population, a positive result is more likely to represent a true case.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">When a disease is rare, the PPV can fall dramatically.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is why a test may perform differently in a specialist clinic than in the general population.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Patients referred to a specialist may already have symptoms or risk factors, increasing their pre-test probability.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Why Doctors Order Confirmatory Tests<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">When an initial test is positive, especially for a rare or serious disease, doctors may perform another test.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The second test may use:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A different laboratory method<\/li>\n\n\n\n<li>Imaging<\/li>\n\n\n\n<li>Genetic analysis<\/li>\n\n\n\n<li>Biopsy<\/li>\n\n\n\n<li>Repeat measurement<\/li>\n\n\n\n<li>A more specific diagnostic technique<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This step can sharply improve diagnostic confidence.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If two independent tests both strongly support the same diagnosis, the probability can rise substantially.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Confirmatory testing helps prevent a single imperfect result from being treated as definitive evidence.<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Bayes\u2019 Theorem and Cancer Screening<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Cancer screening provides a useful example.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A screening result may indicate increased suspicion, but it often does not prove cancer is present.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Additional imaging or tissue sampling may be required.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is because screening programs usually aim to detect as many potentially important cases as possible.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That can mean accepting some false positives.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The balance is difficult.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Too many false positives create anxiety and unnecessary procedures.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Too many false negatives allow real disease to go undetected.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Bayes\u2019 Theorem and Infectious Disease Testing<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The same principle applies to infectious diseases.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If an infection is widespread in a community, a positive test may have a high probability of being correct.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If the infection is extremely uncommon, an unexpected positive result may deserve confirmation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Timing also matters.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Testing too early or too late can influence detection.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Symptoms, exposure history, vaccination status, and local disease prevalence can all change the interpretation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The result is therefore never completely separate from clinical context.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Expert Perspective<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Clinicians and medical statisticians emphasize that <strong>tests should be interpreted as probability-changing tools rather than simple yes-or-no machines<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A result has meaning only when combined with the patient\u2019s history, symptoms, risk factors, and the known performance of the test.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is why experienced doctors do not usually diagnose complex diseases from one isolated number.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Bayesian reasoning is built into good clinical practice even when no one explicitly writes down Bayes\u2019 theorem.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">False Negatives Matter Too<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Bayesian reasoning is not only about positive results.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A negative test can also be misleading.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">If a person has a very high pre-test probability of disease, one negative result may not reduce the probability enough to rule the condition out.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For example, if symptoms are highly characteristic, a doctor may repeat the test or use a different diagnostic method.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>The starting probability matters whether the result is positive or negative.<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Why Testing Everyone Can Sometimes Cause Problems<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">It may seem logical to test as many people as possible for as many diseases as possible.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">But widespread testing can create unintended consequences.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Very rare abnormalities will occasionally appear simply by chance.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">False positives can lead to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Anxiety<\/li>\n\n\n\n<li>Repeat testing<\/li>\n\n\n\n<li>Additional imaging<\/li>\n\n\n\n<li>Biopsies<\/li>\n\n\n\n<li>Medical costs<\/li>\n\n\n\n<li>Incidental findings<\/li>\n\n\n\n<li>Unnecessary treatment<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">This does not mean screening is bad.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It means screening is most useful when the expected benefits outweigh the risks of false alarms and unnecessary intervention.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The Role of Natural Frequencies<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Bayesian problems are often easier to understand using actual numbers rather than percentages.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Instead of saying:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cThe disease prevalence is 0.1%, sensitivity is 99%, and specificity is 99%,\u201d<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">imagine 100,000 real people.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Then count how many are sick, how many healthy people falsely test positive, and how many positive results exist in total.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This approach is called using <strong>natural frequencies<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It often makes probability much easier to understand.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Bayes\u2019 Theorem Beyond Diagnosis<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Bayesian reasoning appears throughout medicine.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Doctors continually update probabilities when new evidence appears.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A symptom changes the probability of a disease.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A blood test changes it again.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">An imaging scan changes it again.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A biopsy may change it further.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Medicine therefore involves repeated updating.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Diagnosis is often a sequence of probability revisions rather than one dramatic moment of certainty.<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Why a Positive Test Should Be Interpreted Calmly<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A positive result can be important and should not be ignored.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">But it should also not automatically be interpreted as a final diagnosis.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The next step depends on the test, condition, symptoms, and clinical situation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For rare diseases in particular, confirmation may be essential.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Understanding Bayes\u2019 theorem can reduce unnecessary panic while also showing why follow-up matters.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The correct question is not simply:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201cWas the test positive?\u201d<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It is:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>\u201cGiven everything we know before and after this test, how likely is the disease now?\u201d<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Interesting Facts<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A test with 99% sensitivity and 99% specificity can still produce mostly false positives when screening for an extremely rare disease.<\/li>\n\n\n\n<li>Disease prevalence strongly influences the positive predictive value of a medical test.<\/li>\n\n\n\n<li>The same laboratory result can mean different things in different patients.<\/li>\n\n\n\n<li>Screening tests are often designed to identify possible disease rather than establish a final diagnosis.<\/li>\n\n\n\n<li>Doctors routinely use Bayesian reasoning even when they do not explicitly calculate Bayes\u2019 theorem.<\/li>\n\n\n\n<li>A negative test does not always rule out disease when the pre-test probability is high.<\/li>\n\n\n\n<li>Confirmatory testing can dramatically improve diagnostic certainty.<\/li>\n\n\n\n<li>Natural frequencies often make medical probability easier to understand than percentages.<\/li>\n\n\n\n<li>Population-wide testing can produce large numbers of false positives even with very good tests.<\/li>\n\n\n\n<li>Clinical history can sometimes be as important as the test result itself.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Glossary<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Bayes\u2019 Theorem<\/strong> \u2014 A mathematical rule used to update the probability of an event when new evidence becomes available.<\/li>\n\n\n\n<li><strong>Pre-Test Probability<\/strong> \u2014 The estimated likelihood that a patient has a condition before a test is performed.<\/li>\n\n\n\n<li><strong>Post-Test Probability<\/strong> \u2014 The estimated likelihood of a condition after taking the test result into account.<\/li>\n\n\n\n<li><strong>Sensitivity<\/strong> \u2014 The proportion of people with a disease who correctly receive a positive test result.<\/li>\n\n\n\n<li><strong>Specificity<\/strong> \u2014 The proportion of people without a disease who correctly receive a negative result.<\/li>\n\n\n\n<li><strong>False Positive<\/strong> \u2014 A positive test result in someone who does not actually have the condition.<\/li>\n\n\n\n<li><strong>False Negative<\/strong> \u2014 A negative test result in someone who actually has the condition.<\/li>\n\n\n\n<li><strong>Positive Predictive Value<\/strong> \u2014 The probability that a person with a positive test truly has the disease.<\/li>\n\n\n\n<li><strong>Prevalence<\/strong> \u2014 The proportion of a population that has a particular condition at a given time.<\/li>\n\n\n\n<li><strong>Screening<\/strong> \u2014 Testing people, often without symptoms, to identify possible disease at an early stage.<\/li>\n\n\n\n<li><strong>Confirmatory Test<\/strong> \u2014 A follow-up test used to verify an initial result.<\/li>\n\n\n\n<li><strong>Natural Frequencies<\/strong> \u2014 A way of expressing probabilities using counts of people or events rather than percentages.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>A positive medical test can be frightening. Many people naturally assume that if a test comes back positive, the disease is almost certainly present. But that is not always true.&hellip;<\/p>\n","protected":false},"author":2,"featured_media":4005,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_sitemap_exclude":false,"_sitemap_priority":"","_sitemap_frequency":"","footnotes":""},"categories":[65,74,60],"tags":[],"class_list":["post-4004","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-be-smart","category-news","category-science"],"_links":{"self":[{"href":"https:\/\/science-x.net\/index.php?rest_route=\/wp\/v2\/posts\/4004","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/science-x.net\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/science-x.net\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/science-x.net\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/science-x.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4004"}],"version-history":[{"count":1,"href":"https:\/\/science-x.net\/index.php?rest_route=\/wp\/v2\/posts\/4004\/revisions"}],"predecessor-version":[{"id":4006,"href":"https:\/\/science-x.net\/index.php?rest_route=\/wp\/v2\/posts\/4004\/revisions\/4006"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/science-x.net\/index.php?rest_route=\/wp\/v2\/media\/4005"}],"wp:attachment":[{"href":"https:\/\/science-x.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4004"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/science-x.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4004"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/science-x.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4004"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}