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Table 2 Description of testing on multiple computer-coded verbal autopsy methods and datasets

From: Performance of four computer-coded verbal autopsy methods for cause of death assignment compared with physician coding on 24,000 deaths in low- and middle-income countries

Dataset Training/testing cases Number of diagnostic indicators
   King-Lu Open-source random forest Open-source tariff method s InterVA-4
China 1100 / 400 48 48 48 N/A
Institute for Health Metrics and Evaluation 1100 / 400 96 96 96 N/A
Million Death Study 1100 / 400 89 89 89 N/A
  1100 / 1100 89 89 89 N/A
  6100 / 6100a 89 89 89 245
Agincourt 1100 / 400 104 104 104 245b
  1100 / 1100 104 104 104 245
  2900 / 2900 104 104 104 245
Matlab 1100 / 400 224 224 224 245
  1100 / 1100 224 224 224 245
  1600 / 1600 224 224 224 245
  1. Only the numbers of test cases are applicable for the InterVA-4 analyses, as this method does not require any training cases. Additionally, InterVA-4 requires the input of 245 diagnostic indicators, however as many of these were not available in the given datasets, the number of useable variables was lower than 245. aThe MDS dataset used for InterVA-4 contained 552 cases, in which we extracted additional InterVA-4 indicators from the narratives. bEach CCVA method ran 30 resamples for each training/testing split within each dataset, except InterVA-4, which used the following number of re-samples: 1 for MDS data; 8, 7, 6 for Agincourt data splits of 400, 1100, and 2900 test cases; and 10, 10, 10 for Matlab data splits of 400, 1100, and 1600 test cases, respectively.