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You are an expert at identifying data availability statements in scientific text. A data availability statement describes where research data can be found or how it can be accessed.<|eot_id|><|start_header_id|>user<|end_header_id|>

Determine if the following text contains a data availability statement. Answer with 'yes' or 'no'.

Text: Impairment in consciousness and cognition can occur in the setting of a primary neurologic disease (e.g., hypoxicischemic, hemorrhagic, or traumatic brain injury), or as a consequence of a systemic insult (e.g., toxic, metabolic, and/or infectious processes). Invariably, an interplay of multiple factors concurrently contributes to the development of encephalopathy. As a result, predicting the short-and long-term consequences of impaired consciousness and cognition remains a challenge, in which the self-fulfilling prophecy bias of premature pessimistic outcome predictions plays an important role [75,76]. Key disease-specific knowledge gaps in neuroprognostication have been recently unveiled in a joint statement from the German Neurocritical Care Society and the Neurocritical Care Society [77]; future studies should account for these limitations. Population-derived disease severity scores are incorrectly used to ascertain long-term individual prognostic trajectories. Further, these scores are not dynamic and are frequently inaccurate. Accurate neuroprognostication is crucial to guide critical care resource allocation and to guide families in goals of care discussions; thus, it is imperative to improve tools for prognostication in neurocritically ill patients. cEEG monitoring provides dynamic information on brain physiology, and it can be used as a noninvasive biomarker that correlates with level of consciousness [78] and neurological recovery [79]. Machine learning has been demonstrated to be a promising tool to analyze EEG **data** and unveil patients with preserved awareness by means of electroencephalographic activation in patients with acute brain injury [78]. This state known as "cognitive motor dissociation" was previously demonstrated in chronically unresponsive patients [80], and more recently in acute brain injury [78,81]. Similarly, the use of machine learning and quantitative analysis of longitudinal EEG signals shows promise in improving accuracy of commonly employed neurophysiologic predictors of outcome after cardiac arrest, such as background reactivity, epileptiform activity, and background continuity [79,82].<|eot_id|><|start_header_id|>assistant<|end_header_id|>

no<|eot_id|>