Articles: sensitivity-specificity.
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Cochrane Db Syst Rev · Dec 2024
Review Meta AnalysisDiagnostic test accuracy of self-administered cognitive assessment tools for dementia.
Dementia is a chronic and progressive clinical syndrome that can present with a range of cognitive and behavioural symptoms. Global prevalence is projected to increase due to ageing populations, particularly in resource-limited settings, with significant associated health and social care costs. There is a critical need for accurate cognitive assessment as part of the diagnostic workup for dementia. Although self-administered cognitive assessment tools are not diagnostic, they can be used to assess cognition. The role of these tests is uncertain, and their diagnostic test accuracy remains unclear, but they may be useful tools in circumstances where face-to-face assessment may be difficult. ⋯ There is insufficient evidence to recommend the use of any single self-administered cognitive assessment tool. The tools had test accuracy scores that are similar to the range seen with standard pencil and paper cognitive screening tests conducted by clinicians. Further research on the optimal test and threshold score, and how that may be impacted by setting, language, and educational level is needed.
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Cochrane Db Syst Rev · Dec 2024
Review Meta AnalysisThe effect of sample site and collection procedure on identification of SARS-CoV-2 infection.
Sample collection is a key driver of accuracy in the diagnosis of SARS-CoV-2 infection. Viral load may vary at different anatomical sampling sites and accuracy may be compromised by difficulties obtaining specimens and the expertise of the person taking the sample. It is important to optimise sampling accuracy within cost, safety and accessibility constraints. ⋯ When used with RT-PCR, there is no evidence for a difference in sensitivity of self-collected gargle or deep-throat saliva samples compared to nasopharyngeal samples collected by healthcare workers when used with RT-PCR. Use of these alternative, self-collected sample types has the potential to reduce cost and discomfort and improve the safety of sampling by reducing risk of transmission from aerosol spread which occurs as a result of coughing and gagging during the nasopharyngeal or oropharyngeal sample collection procedure. This may, in turn, improve access to and uptake of testing. Other types of saliva, nasal, oral and oropharyngeal samples are, on average, less sensitive compared to healthcare worker-collected nasopharyngeal samples, and it is unlikely that sensitivities of this magnitude would be acceptable for confirmation of SARS-CoV-2 infection with RT-PCR. When used with Ag-RDTs, there is no evidence of a difference in sensitivity between nasal samples and healthcare worker-collected nasopharyngeal samples for detecting SARS-CoV-2. The implications of this for self-testing are unclear as evaluations did not report whether nasal samples were self-collected or collected by healthcare workers. Further research is needed in asymptomatic individuals, children and in Ag-RDTs, and to investigate the effect of operator expertise on accuracy. Quality assessment of the evidence base underpinning these conclusions was restricted by poor reporting. There is a need for further high-quality studies, adhering to reporting standards for test accuracy studies.
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Meta Analysis
Improving Cancer Probability Estimation in Non-Diagnostic Bronchoscopies: A meta-analysis.
In patients with peripheral pulmonary lesions (PPLs), nondiagnostic bronchoscopy results are not uncommon. The conventional approach to estimate the probability of cancer (pCA) after bronchoscopy relies on dichotomous test assumptions, using prevalence, sensitivity, and specificity to determine negative predictive value. However, bronchoscopy is a multidisease test, raising concerns about the accuracy of dichotomous methods. ⋯ Conventional dichotomous methods for estimating pCA after nondiagnostic bronchoscopies underestimate the likelihood of malignancy. Physicians should opt for the multidisease test approach when interpreting bronchoscopy results.
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Review Meta Analysis
Bedside-focused transthoracic echocardiography in acute atraumatic thoracic aortic syndrome: a systematic review and meta-analysis of diagnostic accuracy.
The objective of this review was to assess the diagnostic accuracy of bedside-focused transthoracic echocardiography (TTE) in acute atraumatic thoracic aortic syndrome in adults. We performed a systematic review and meta-analysis of publications that described the use of bedside-focused TTE on adults presenting to emergency care settings with suspected atraumatic thoracic aortic syndrome. Studies were identified using keyword and MeSH on relevant databases as well as grey literature, followed by abstract screening and study selection by two independent reviewers. ⋯ For type B dissection, pooled sensitivity was 65% (95% CI, 45-80%) and specificity was 100% (95% CI, 0.69-100%). Regarding indirect TTE signs, pooled sensitivities and specificities were 64% (5.2-98.2%) and 94% (92-96.1%), respectively for aortic valve regurgitation, 92% (54-99.2%) and 87% (62-97%) for thoracic aortic aneurysm and 39% (33.8-45%) and 94% (92-95%) for pericardial effusion. In this systematic review and meta-analysis, bedside-focused TTE has high specificity for type A and B dissection, a moderate to high sensitivity for type A but poor for type B, and unclear diagnostic accuracy for intramural haematoma and penetrating aortic ulcer.
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Cochrane Db Syst Rev · Oct 2024
Review Meta AnalysisArtificial intelligence for diagnosing exudative age-related macular degeneration.
Age-related macular degeneration (AMD) is a retinal disorder characterized by central retinal (macular) damage. Approximately 10% to 20% of non-exudative AMD cases progress to the exudative form, which may result in rapid deterioration of central vision. Individuals with exudative AMD (eAMD) need prompt consultation with retinal specialists to minimize the risk and extent of vision loss. Traditional methods of diagnosing ophthalmic disease rely on clinical evaluation and multiple imaging techniques, which can be resource-consuming. Tests leveraging artificial intelligence (AI) hold the promise of automatically identifying and categorizing pathological features, enabling the timely diagnosis and treatment of eAMD. ⋯ Low- to very low-certainty evidence suggests that an algorithm-based test may correctly identify most individuals with eAMD without increasing unnecessary referrals (false positives) in either the primary or the specialty care settings. There were significant concerns for applying the review findings due to variations in the eAMD prevalence in the included studies. In addition, among the included algorithm-based tests, diagnostic accuracy estimates were at risk of bias due to study participants not reflecting real-world characteristics, inadequate model validation, and the likelihood of selective results reporting. Limited quality and quantity of externally validated algorithms highlighted the need for high-certainty evidence. This evidence will require a standardized definition for eAMD on different imaging modalities and external validation of the algorithm to assess generalizability.