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51-404: The older version of the questionnaire contains 9 items in total, answered in a "yes" or "no" format. The first three items (Part A) evaluate alcohol and drug use over the past year and the other six (Part B) ask about situations in which the respondent used drugs or alcohol and any consequences of the usage. The CRAFFT 2.1 screening tool begins with past-12-month frequency items (Part A), rather than

102-420: A " gold standard test " which is assumed correct. For all testing, both diagnoses and screening , there is usually a trade-off between sensitivity and specificity, such that higher sensitivities will mean lower specificities and vice versa. A test which reliably detects the presence of a condition, resulting in a high number of true positives and low number of false negatives, will have a high sensitivity. This

153-426: A clinical setting) refers to the test's ability to correctly detect ill patients out of those who do have the condition. Mathematically, this can be expressed as: A negative result in a test with high sensitivity can be useful for "ruling out" disease, since it rarely misdiagnoses those who do have the disease. A test with 100% sensitivity will recognize all patients with the disease by testing positive. In this case,

204-465: A condition may be subjected to more testing, expense, stigma, anxiety, etc. The terms "sensitivity" and "specificity" were introduced by American biostatistician Jacob Yerushalmy in 1947. There are different definitions within laboratory quality control , wherein "analytical sensitivity" is defined as the smallest amount of substance in a sample that can accurately be measured by an assay (synonymously to detection limit ), and "analytical specificity"

255-484: A false positive rate of 100%, rendering it useless for detecting or "ruling in" the disease. The calculation of sensitivity does not take into account indeterminate test results. If a test cannot be repeated, indeterminate samples either should be excluded from the analysis (the number of exclusions should be stated when quoting sensitivity) or can be treated as false negatives (which gives the worst-case value for sensitivity and may therefore underestimate it). A test with

306-516: A given confidence level (e.g., 95%). In information retrieval , the positive predictive value is called precision , and sensitivity is called recall . Unlike the Specificity vs Sensitivity tradeoff, these measures are both independent of the number of true negatives, which is generally unknown and much larger than the actual numbers of relevant and retrieved documents. This assumption of very large numbers of true negatives versus positives

357-418: A high false positive rate, and it does not reliably identify colorectal cancer in the overall population of asymptomatic people (PPV = 10%). On the other hand, this hypothetical test demonstrates very accurate detection of cancer-free individuals (NPV ≈ 99.5%). Therefore, when used for routine colorectal cancer screening with asymptomatic adults, a negative result supplies important data for

408-483: A higher sensitivity has a lower type II error rate. Consider the example of a medical test for diagnosing a disease. Specificity refers to the test's ability to correctly reject healthy patients without a condition. Mathematically, this can be written as: A positive result in a test with high specificity can be useful for "ruling in" disease, since the test rarely gives positive results in healthy patients. A test with 100% specificity will recognize all patients without

459-509: A negative test result would definitively rule out the presence of the disease in a patient. However, a positive result in a test with high sensitivity is not necessarily useful for "ruling in" disease. Suppose a 'bogus' test kit is designed to always give a positive reading. When used on diseased patients, all patients test positive, giving the test 100% sensitivity. However, sensitivity does not take into account false positives. The bogus test also returns positive on all healthy patients, giving it

510-728: A sensitivity of 96% and specificity of 81% for detecting past-12-month use of any substance, suggesting better performance in identifying substance use compared to that of the "yes/no" questions found in the prior study. The CRAFFT 2.1 has been translated into the following languages: Albanian, Arabic, Burmese, Simplified Chinese, Traditional Chinese, Cape Verdean Creole, Haitian Creole, Dutch, French, German, Greek, Hebrew, Hindi, Japanese, Khmer, Korean, Laotian, Lithuanian, Nepali, Portuguese (Brazil), Portuguese (Portugal), Romanian, Russian, Somali, Spanish (Latin Am), Spanish (Spain), Swahili, Telugu, Turkish, Twi, and Vietnamese. The CRAFFT 2.1+N expands upon

561-484: A third of eighth graders have used alcohol in the past year. These findings also contributed to the identification of a need for a tool like the CRAFFT to be developed and widely implemented. This revised version of the CRAFFT screening tool incorporates changes that enhance the sensitivity of the system in terms of identifying adolescents with substance use, and presents new recommended clinician talking points, informed by

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612-402: Is 37 + 8 = 45, which gives a sensitivity of 37 / 45 = 82.2 %. There are 40 - 8 = 32 TN. The specificity therefore comes out to 32 / 35 = 91.4%. The red dot indicates the patient with the medical condition. The red background indicates the area where the test predicts the data point to be positive. The true positive in this figure is 6, and false negatives of 0 (because all positive condition

663-400: Is correctly predicted as positive). Therefore, the sensitivity is 100% (from 6 / (6 + 0) ). This situation is also illustrated in the previous figure where the dotted line is at position A (the left-hand side is predicted as negative by the model, the right-hand side is predicted as positive by the model). When the dotted line, test cut-off line, is at position A, the test correctly predicts all

714-405: Is defined as the ability of an assay to measure one particular organism or substance, rather than others. However, this article deals with diagnostic sensitivity and specificity as defined at top. Imagine a study evaluating a test that screens people for a disease. Each person taking the test either has or does not have the disease. The test outcome can be positive (classifying the person as having

765-438: Is defined as: An estimate of d′ can be also found from measurements of the hit rate and false-alarm rate. It is calculated as: where function Z ( p ), p ∈ [0, 1], is the inverse of the cumulative Gaussian distribution . d′ is a dimensionless statistic. A higher d′ indicates that the signal can be more readily detected. The relationship between sensitivity, specificity, and similar terms can be understood using

816-414: Is especially important when the consequence of failing to treat the condition is serious and/or the treatment is very effective and has minimal side effects. A test which reliably excludes individuals who do not have the condition, resulting in a high number of true negatives and low number of false positives, will have a high specificity. This is especially important when people who are identified as having

867-450: Is not applicable in the present context. A sensitive test will have fewer Type II errors . Similarly to the domain of information retrieval , in the research area of gene prediction , the number of true negatives (non-genes) in genomic sequences is generally unknown and much larger than the actual number of genes (true positives). The convenient and intuitively understood term specificity in this research area has been frequently used with

918-483: Is often claimed that a highly specific test is effective at ruling in a disease when positive, while a highly sensitive test is deemed effective at ruling out a disease when negative. This has led to the widely used mnemonics SPPIN and SNNOUT, according to which a highly sp ecific test, when p ositive, rules in disease (SP-P-IN), and a highly s e n sitive test, when n egative, rules out disease (SN-N-OUT). Both rules of thumb are, however, inferentially misleading, as

969-402: Is rare in other applications. The F-score can be used as a single measure of performance of the test for the positive class. The F-score is the harmonic mean of precision and recall: In the traditional language of statistical hypothesis testing , the sensitivity of a test is called the statistical power of the test, although the word power in that context has a more general usage that

1020-468: Is scored as "1" point and a CRAFFT total score of two or higher identifies "high risk" for a substance use disorder and warrants further assessment. The CRAFFT Screening Test was developed by John R Knight, MD and colleagues at the Center for Adolescent Behavioral Health Research (CABHRe), formerly known as the Center for Adolescent Substance Abuse Research (CeASAR) at Boston Children's Hospital. Their goal

1071-622: Is supported by many studies as a reliable and valid assessment of substance use and misuse in adolescents and is considered an effective tool for assessing whether further assessment is warranted. It has been well-validated against criterion standard psychological tests and structured psychiatric diagnostic interviews. It has been recommended by the American Academy of Pediatrics ' Committee on Substance Abuse for use with adolescents. Findings suggest that pediatricians should regularly screen for substance use disorders in adolescents using

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1122-429: Is then 26, and the number of false positives is 0. This result in 100% specificity (from 26 / (26 + 0) ). Therefore, sensitivity or specificity alone cannot be used to measure the performance of the test. In medical diagnosis , test sensitivity is the ability of a test to correctly identify those with the disease (true positive rate), whereas test specificity is the ability of the test to correctly identify those without

1173-461: Is zero at that line, meaning all the positive test results are true positives. The middle solid line in both figures above that show the level of sensitivity and specificity is the test cutoff point. As previously described, moving this line results in a trade-off between the level of sensitivity and specificity. The left-hand side of this line contains the data points that tests below the cut off point and are considered negative (the blue dots indicate

1224-423: The gold standard four times, but a single additional test against the gold standard that gave a poor result would imply a sensitivity of only 80%. A common way to do this is to state the binomial proportion confidence interval , often calculated using a Wilson score interval. Confidence intervals for sensitivity and specificity can be calculated, giving the range of values within which the correct value lies at

1275-457: The CRAFFT includes a risk item to evaluate whether an adolescent has ever ridden in a car driven by someone (including themselves) who was under the influence of alcohol or other drugs. It has been established as valid and reliable for identifying youth who need further assessment and therapeutic intervention The CRAFFT was originally designed to screen adolescents at high risk of substance use disorders in primary medical care offices. However,

1326-580: The CRAFFT. The CRAFFT has been translated into many languages, including Albanian, Arabic, Burmese, Simplified Chinese, Traditional Chinese, Cape Verdean Creole, Haitian Creole, Dutch, French, German, Greek, Hebrew, Hindi, Japanese, Khmer, Korean, Laotian, Lithuanian, Nepali, Portuguese (Brazil), Portuguese (Portugal), Romanian, Russian, Somali, Spanish (Latin Am), Spanish (Spain), Swahili, Telugu, Turkish, Twi, and Vietnamese. Studies attest to its validity and reliability across cultures. CAGE questionnaire Too Many Requests If you report this error to

1377-562: The False Negatives (FN), the white dots True Negatives (TN)). The right-hand side of the line shows the data points that tests above the cut off point and are considered positive (red dots indicate False Positives (FP)). Each side contains 40 data points. For the figure that shows high sensitivity and low specificity, there are 3 FN and 8 FP. Using the fact that positive results = true positives (TP) + FP, we get TP = positive results - FP, or TP = 40 - 8 = 32. The number of sick people in

1428-490: The Wikimedia System Administrators, please include the details below. Request from 172.68.168.236 via cp1112 cp1112, Varnish XID 975424508 Upstream caches: cp1112 int Error: 429, Too Many Requests at Thu, 28 Nov 2024 11:05:11 GMT Sensitivity and specificity In medicine and statistics , sensitivity and specificity mathematically describe the accuracy of a test that reports

1479-441: The behavior, and may thus mitigate discomfort around disclosure. The instruction, "Say '0' if none" follows each question to convey that non-use is also normative. The CRAFFT 2.1 begins with past-12-month frequency items; i.e., "During the past 12 months, on how many days did you … [drink/use substance name]?" This new set of frequency questions was tested in a recent study of 708 adolescent primary care patients ages 12–18 that found

1530-542: The content from the CRAFFT 2.1 with the inclusion of the Hooked On Nicotine Checklist (HONC), which is a 10-item questionnaire that screens for dependence on tobacco and nicotine. If a teen indicates use of a vaping device containing nicotine and/or flavors or any tobacco products within the frequency questions, they are prompted to answer the HONC questions as well. A positive response to one or more of

1581-434: The data set is equal to TP + FN, or 32 + 3 = 35. The sensitivity is therefore 32 / 35 = 91.4%. Using the same method, we get TN = 40 - 3 = 37, and the number of healthy people 37 + 8 = 45, which results in a specificity of 37 / 45 = 82.2 %. For the figure that shows low sensitivity and high specificity, there are 8 FN and 3 FP. Using the same method as the previous figure, we get TP = 40 - 3 = 37. The number of sick people

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1632-564: The diagnostic power of any test is determined by the prevalence of the condition being tested, the test's sensitivity and its specificity. The SNNOUT mnemonic has some validity when the prevalence of the condition in question is extremely low in the tested sample. The tradeoff between specificity and sensitivity is explored in ROC analysis as a trade off between TPR and FPR (that is, recall and fallout ). Giving them equal weight optimizes informedness = specificity + sensitivity − 1 = TPR − FPR,

1683-424: The disease (true negative rate). If 100 patients known to have a disease were tested, and 43 test positive, then the test has 43% sensitivity. If 100 with no disease are tested and 96 return a completely negative result, then the test has 96% specificity. Sensitivity and specificity are prevalence-independent test characteristics, as their values are intrinsic to the test and do not depend on the disease prevalence in

1734-436: The disease by testing negative, so a positive test result would definitively rule in the presence of the disease. However, a negative result from a test with high specificity is not necessarily useful for "ruling out" disease. For example, a test that always returns a negative test result will have a specificity of 100% because specificity does not consider false negatives. A test like that would return negative for patients with

1785-399: The disease is likely to be classified as positive by the test. On the other hand, if the specificity is high, any person who does not have the disease is likely to be classified as negative by the test. An NIH web site has a discussion of how these ratios are calculated. Consider the example of a medical test for diagnosing a condition. Sensitivity (sometimes also named the detection rate in

1836-411: The disease) or negative (classifying the person as not having the disease). The test results for each subject may or may not match the subject's actual status. In that setting: After getting the numbers of true positives, false positives, true negatives, and false negatives, the sensitivity and specificity for the test can be calculated. If it turns out that the sensitivity is high then any person who has

1887-467: The disease, making it useless for "ruling out" the disease. A test with a higher specificity has a lower type I error rate. The above graphical illustration is meant to show the relationship between sensitivity and specificity. The black, dotted line in the center of the graph is where the sensitivity and specificity are the same. As one moves to the left of the black dotted line, the sensitivity increases, reaching its maximum value of 100% at line A, and

1938-535: The following table. Consider a group with P positive instances and N negative instances of some condition. The four outcomes can be formulated in a 2×2 contingency table or confusion matrix , as well as derivations of several metrics using the four outcomes, as follows: Related calculations This hypothetical screening test (fecal occult blood test) correctly identified two-thirds (66.7%) of patients with colorectal cancer. Unfortunately, factoring in prevalence rates reveals that this hypothetical test has

1989-603: The items calls for further assessment regarding a serious problem with nicotine. Research has shown that CRAFFT has relatively high sensitivity and specificity , internal consistency , and test-retest reliability as a screener for alcohol and substance misuse. The CRAFFT questionnaire has been validated against the current edition of the Diagnostic and Statistical Manual of Mental Disorders ( DSM-5 ) and demonstrates good ability to distinguish between those with and without clinical levels of any DSM-5 substance use disorder . It

2040-474: The latest science and clinician feedback, to guide a brief discussion about substance use with adolescents. The CRAFFT 2.1 provides an updated and revised version of this well-validated and widely utilized adolescent substance use screening protocol. Although the previous version of the CRAFFT will still be available, CABHRe recommends that clinicians transition to using version 2.1. The CRAFFT 2.1 screening tool begins with past-12-month frequency items, rather than

2091-400: The magnitude of which gives the probability of an informed decision between the two classes (> 0 represents appropriate use of information, 0 represents chance-level performance, < 0 represents perverse use of information). The sensitivity index or d′ (pronounced "dee-prime") is a statistic used in signal detection theory . It provides the separation between the means of

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2142-462: The necessity for a universal adolescent screening measure was made apparent by research findings suggesting that half of high school students drink, a third binge drink, and a fourth use marijuana. For drug use specifically, studies show that more than half of high school seniors have used an illegal drug of any kind and a fourth have used illegal drugs other than marijuana . In addition, more than two-thirds of high school seniors, half of sophomores, and

2193-433: The patient and doctor, such as ruling out cancer as the cause of gastrointestinal symptoms or reassuring patients worried about developing colorectal cancer. Sensitivity and specificity values alone may be highly misleading. The 'worst-case' sensitivity or specificity must be calculated in order to avoid reliance on experiments with few results. For example, a particular test may easily show 100% sensitivity if tested against

2244-417: The population of interest. Positive and negative predictive values , but not sensitivity or specificity, are values influenced by the prevalence of disease in the population that is being tested. These concepts are illustrated graphically in this applet Bayesian clinical diagnostic model which show the positive and negative predictive values as a function of the prevalence, sensitivity and specificity. It

2295-411: The population of the true positive class, but it will fail to correctly identify the data point from the true negative class. Similar to the previously explained figure, the red dot indicates the patient with the medical condition. However, in this case, the green background indicates that the test predicts that all patients are free of the medical condition. The number of data point that is true negative

2346-423: The presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true positives and specificity is a measure of how well a test can identify true negatives: If the true status of the condition cannot be known, sensitivity and specificity can be defined relative to

2397-492: The previous "yes/no" question for any use over the past year, and the other six (Part B) questions remain the same. The CRAFFT can function as a self-report questionnaire or an interview to be administered by a clinician. Both employ a skip pattern: those whose Part A score is "0" (no use) answer the C ar question only of Part B, while those who report any use in Part A also answer all six Part B CRAFFT questions. Each "yes" answer

2448-587: The previous "yes/no" question for any use over the past year. A recent study examining these opening yes/no questions found that they had relatively low sensitivity in identifying youth with any past-12-month alcohol or marijuana use (62% and 72%, respectively). Research also has suggested that yes/no questions may contribute to lower sensitivity on certain measures by inhibiting disclosure of less socially desirable behaviors; i.e., they may be more prone to social desirability bias. Alternatively, questions that ask "how many" or "how often" implicitly imply an expectation of

2499-518: The signal and the noise distributions, compared against the standard deviation of the noise distribution. For normally distributed signal and noise with mean and standard deviations μ S {\displaystyle \mu _{S}} and σ S {\displaystyle \sigma _{S}} , and μ N {\displaystyle \mu _{N}} and σ N {\displaystyle \sigma _{N}} , respectively, d′

2550-399: The specificity decreases. The sensitivity at line A is 100% because at that point there are zero false negatives, meaning that all the negative test results are true negatives. When moving to the right, the opposite applies, the specificity increases until it reaches the B line and becomes 100% and the sensitivity decreases. The specificity at line B is 100% because the number of false positives

2601-501: Was to develop a screening tool that - like the CAGE questionnaire used for adults - was brief and easy to administer and score. Unlike the CAGE, the CRAFFT was designed to be developmentally appropriate for adolescents and screen conjointly for both alcohol and drug use. Because motor vehicle crashes are a leading cause of death among adolescents, and often associated with alcohol and drug use,

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