Not yet reviewed by Pith; the record is open.

[Edited for arXiv] Source extraction in HI radio surveys is still often performed using visual inspection, but the efficacy of such procedures lacks rigorous quantitative assessment due to their laborious nature. Algorithmic methods are often preferred due to their repeatable results and speed. I here quantitatively assess visual source extraction using a large sample of artificial sources and a comparatively rapid source extraction tool, and compare the results with those from automatic techniques. I injected 4,232 sources into a total of 8,500 emission-free data cubes, with at most one source per cube. Sources covered a wide range of signal-to-noise and velocity widths. I blindly searched all cubes, measuring the completeness and reliability for pairs of signal-to-noise and line width values. Smaller control tests were performed to account for the possible biases in the search, which gave results in good agreement with the main experiment. I also searched cubes injected with artificial sources using algorithmic extractors, and compare these results with a set of catalogues independently reported from real observational data. I find that the results of visual extraction follow a tight relation between integrated signal-to-noise and completeness. Visual extraction compares favourably in efficacy with the algorithmic methods, tending to recover a higher fraction of fainter sources. Visual source extraction can be a surprisingly rapid procedure which gives higher completeness levels than automatic techniques, giving predictable, quantifiable results which are not strongly subject to the whims of the observer. For recovering the faintest features, algorithmic extractors can be competitive with visual inspection but cannot yet out-perform it, though their advantage in speed can be a significant compensating factor.