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The Childhood Autism Rating Scale ( CARS) is a behavior rating scale intended to help diagnose autism. CARS was developed by Eric Schopler, Robert J. Reichler, and Barbara Rochen Renner. The scale was designed to help differentiate children with autism from those with other developmental delays, such as intellectual disability .
On-board diagnostics. Various views of a "MaxScan OE509" – a fairly typical onboard diagnostics (OBD) scanner, 2015. On-board diagnostics ( OBD) is a term referring to a vehicle's self-diagnostic and reporting capability. In the United States, this capability is a requirement to comply with federal emissions standards to detect failures that ...
OBD-II PIDs ( On-board diagnostics Parameter IDs) are codes used to request data from a vehicle, used as a diagnostic tool. SAE standard J1979 defines many OBD-II PIDs. All on-road vehicles and trucks sold in North America are required to support a subset of these codes, primarily for state mandated emissions inspections.
ELM327. The ELM327 is a programmed microcontroller produced for translating the on-board diagnostics (OBD) interface found in most modern cars. The ELM327 command protocol is one of the most popular PC-to-OBD interface standards and is also implemented by other vendors. The original ELM327 was implemented on the PIC18F2480 microcontroller from ...
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A 2018 Cochrane systematic review included 12 studies of ADOS diagnostic accuracy in pre-school children (Modules 1 and 2). The summary sensitivity was 0.94 (95% CI 0.89 to 0.97), with sensitivity in individual studies ranging from 0.76 to 0.98. The summary specificity was 0.80 (95% CI 0.68 to 0.88), with specificity in individual studies ...
Diagnostic coding is the translation of written descriptions of diseases, illnesses and injuries into codes from a particular classification. In medical classification, diagnosis codes are used as part of the clinical coding process alongside intervention codes. Both diagnosis and intervention codes are assigned by a health professional trained ...
In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. For example, an innocent person may be convicted. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. For example: a guilty person may be not convicted.