Table 1.

Item list used to extract eligible papers

Item groups	Item list	Detailed items
General characteristics	Diagnostic task	What is the target condition?
	Study objective	Is the study aiming at the development of a diagnostic method, evaluation of a diagnostic method or both?
	Target population	What is the population targeted by the diagnostic test?
Methods	Data sources	Where and when potentially eligible participants were identified (setting, location and dates)
	Data split	Method for partitioning the evaluation set from the training data. To assess whether participants formed a consecutive, random or convenience series.
	Test dataset eligibility criteria	On what basis potentially eligible participants were identified within the test dataset (such as symptoms, results from previous tests, inclusion in registry).
Results	Baseline characteristics	Baseline demographic and clinical characteristics of participants
	Diagnosis/non-diagnosis classification	Classification of the diagnosed and non-diagnosed patients within the test set.
	Flow diagram	Flow of participants, using a diagram.
	Severity	Distribution of severity of disease in those with the target condition.
	Alternative diagnosis	Distribution of alternative diagnoses in those without the target condition.
	Difference between reference test and ML test	Is there a time interval between index test and reference standard?
	Applicability	Does the evaluation population correspond to the setting in which the diagnosis test will be applied?

ML, machine learning.