Measure  #  Definition  Formula 

Recall (sensitivity)  22  Proportion of correctly identified positives amongst all real positives 

Precision  18  Proportion of correctly identified positives amongst all positives. 

F measure  10  Combines precision and recall. Values of β < 1.0 indicate precision is more important than recall, whilst values of β > 1.0 indicate recall is more important than precision  Where β is a value that specifies the relative importance of recall and precision. 
ROC (AUC)  10  Area under the curve traced out by graphing the true positive rate against the false positive rate. 1.0 is a perfect score and 0.50 is equivalent to a random ordering  
Accuracy  8  Proportion of agreements to total number of documents. 

Work saved over sampling  8  The percentage of papers that the reviewers do not have to read because they have been screened out by the classifier 

Time  7  Time taken to screen (usually in minutes)  
Burden  4  The fraction of the total number of items that a human must screen (active learning) 

Yield  3  The fraction of items that are identified by a given screening approach (active learning) 

Utility  5  Relative measure of burden and yield that takes into account reviewer preferences for weighting these two concepts (active learning)  Where β is the userdefined weight 
Baseline inclusion rate  2  The proportion of includes in a random sample of items before prioritisation or classification takes place. The number to be screened is determined using a power calculation  Where n_{ i } = number of items included in the random sample; n_{ t } = total number of items in the random sample 
Performance (efficiency) ^{a}  2  Number of relevant items selected divided by the time spent screening, where relevant items were those marked as included by two or more people 

Specificity  2  The proportion of correctly identified negatives (excludes) out of the total number of negatives 

True positives  2  The number of correctly identified positives (includes)  TP 
False negatives  1  The number of incorrectly identified negatives (excludes)  FN 
Coverage  1  The ratio of positives in the data pool that are annotated during active learning  Where L refers to labelled items and U refers to unlabelled items 
Unit cost  1  Expected time to label an item multiplied by the unit cost of the labeler (salary per unit of time), as calculated from their (known or estimated) salary  time_{expected} × cost_{unit} 
Classification error  1  Proportion of disagreements to total number of documents  100 % − accuracy % 
Error  1  Total number of falsely classified items divided by the total number of items 

Absolute screening reduction  1  Number of items excluded by the classifier that do not need to be manually screened  TN + FN 
Prioritised inclusion rate  1  The proportion of includes out of the total number screened, after prioritisation or classification takes place  Where n_{ip} = number of items included in prioritised sample; n_{tp} = total number of items in the prioritised sample 