Table 1

Item list used to extract eligible papers

Item groupsItem listDetailed items
General characteristicsDiagnostic taskWhat is the target condition?
Study objectiveIs the study aiming at the development of a diagnostic method, evaluation of a diagnostic method or both?
Target populationWhat is the population targeted by the diagnostic test?
MethodsData sourcesWhere and when potentially eligible participants were identified (setting, location and dates)
Data splitMethod for partitioning the evaluation set from the training data. To assess whether participants formed a consecutive, random or convenience series.
Test dataset eligibility criteriaOn what basis potentially eligible participants were identified within the test dataset (such as symptoms, results from previous tests, inclusion in registry).
ResultsBaseline characteristicsBaseline demographic and clinical characteristics of participants
Diagnosis/non-diagnosis classificationClassification of the diagnosed and non-diagnosed patients within the test set.
Flow diagramFlow of participants, using a diagram.
SeverityDistribution of severity of disease in those with the target condition.
Alternative diagnosisDistribution of alternative diagnoses in those without the target condition.
Difference between reference test and ML testIs there a time interval between index test and reference standard?
ApplicabilityDoes the evaluation population correspond to the setting in which the diagnosis test will be applied?
  • ML, machine learning.