Accurate medical triage is essential for improving patient outcomes and efficient healthcare delivery. Patients increasingly rely on artificial intelligence (AI)-based applications to access healthcare information, including medical triage advice. We assessed the accuracy of triage decisions provided by an AI-based application. We presented 50 clinical vignettes to the AI-based application, seven emergency medicine providers, and five internal medicine physicians. We compared the triage decisions of the AI-based application to those of the individual providers as well as their consensus decisions. When compared to the human clinicians\' consensus triage decisions, the AI-based application performed equal or better than individual human clinicians.

BACKGROUND: Previous studies have shown that a single Coma-Recovery Scale-Revision (CRS-R) assessment can identify high rates of misdiagnosis by clinical consensus. The aim of this study was to investigate the proportion of misdiagnosis by clinical consensus compared to repeated behavior-scale assessments in patients with prolonged disorders of consciousness (DOC).
METHODS: Patients with prolonged DOC during hospitalization were screened by clinicians, and the clinicians formed a clinical-consensus diagnosis. Trained professionals used the CRS-R to evaluate the consciousness levels of the enrolled patients repeatedly (≥5 times) within a week. Based on the repeated evaluation results, the enrolled patients with prolonged DOC were divided into unresponsive wakefulness syndrome (UWS), minimally conscious state (MCS), and emergence from MCS (EMCS). Finally, the relationship between the results of the CRS-R and the clinical consensus were analyzed.
RESULTS: In this study, 137 patients with a clinical-consensus diagnosis of prolonged DOC were enrolled. It was found that 24.7% of patients with clinical UWS were actually in MCS after a single CRS-R behavior evaluation, while the repeated CRS-R evaluation results showed that the proportion of misdiagnosis of MCS was 38.2%. A total of 16.7% of EMCS patients were misdiagnosed with clinical MCS, and 1.1% of EMCS patients were misdiagnosed with clinical UWS.
CONCLUSIONS: The rate of the misdiagnosis by clinical consensus is still relatively high. Therefore, clinicians should be aware of the importance of the bedside CRS-R behavior assessment and should apply the CRS-R tool in daily procedures.
BACKGROUND: ClinicalTrials.gov ID: NCT04139239 ; Registered 24 October 2019 - Retrospectively registered.

The concept that disease rooted principally in chronic aberrant constitutive and reactive activation of mast cells (MCs), without the gross MC neoplasia in mastocytosis, first emerged in the 1980s, but only in the last decade has recognition of \"mast cell activation syndrome\" (MCAS) grown significantly. Two principal proposals for diagnostic criteria have emerged. One, originally published in 2012, is labeled by its authors as a \"consensus\" (re-termed here as \"consensus-1\"). Another sizable contingent of investigators and practitioners favor a different approach (originally published in 2011, newly termed here as \"consensus-2\"), resembling \"consensus-1\" in some respects but differing in others, leading to substantial differences between these proposals in the numbers of patients qualifying for diagnosis (and thus treatment). Overdiagnosis by \"consensus-2\" criteria has potential to be problematic, but underdiagnosis by \"consensus-1\" criteria seems the far larger problem given (1) increasing appreciation that MCAS is prevalent (up to 17% of the general population), and (2) most MCAS patients, regardless of illness duration prior to diagnosis, can eventually identify treatment yielding sustained improvement. We analyze these proposals (and others) and suggest that, until careful research provides more definitive answers, diagnosis by either proposal is valid, reasonable, and helpful.

OBJECTIVE: Use of small changes in serum creatinine to diagnose AKI allows for earlier detection but may increase diagnostic false-positive rates because of inherent laboratory and biologic variabilities of creatinine.
METHODS: We examined serum creatinine measurement characteristics in a prospective observational clinical reference cohort of 2267 adult patients with AKI by Kidney Disease Improving Global Outcomes creatinine criteria and used these data to create a simulation cohort to model AKI false-positive rates. We simulated up to seven successive blood draws on an equal population of hypothetical patients with unchanging true serum creatinine values. Error terms generated from laboratory and biologic variabilities were added to each simulated patient\'s true serum creatinine value to obtain the simulated measured serum creatinine for each blood draw. We determined the proportion of patients who would be erroneously diagnosed with AKI by Kidney Disease Improving Global Outcomes creatinine criteria.
RESULTS: Within the clinical cohort, 75.0% of patients received four serum creatinine draws within at least one 48-hour period during hospitalization. After four simulated creatinine measurements that accounted for laboratory variability calculated from assay characteristics and 4.4% of biologic variability determined from the clinical cohort and publicly available data, the overall false-positive rate for AKI diagnosis was 8.0% (interquartile range =7.9%-8.1%), whereas patients with true serum creatinine ≥1.5 mg/dl (representing 21% of the clinical cohort) had a false-positive AKI diagnosis rate of 30.5% (interquartile range =30.1%-30.9%) versus 2.0% (interquartile range =1.9%-2.1%) in patients with true serum creatinine values <1.5 mg/dl (P<0.001).
CONCLUSIONS: Use of small serum creatinine changes to diagnose AKI is limited by high false-positive rates caused by inherent variability of serum creatinine at higher baseline values, potentially misclassifying patients with CKD in AKI studies.