. 2021 May 5;102(2):1025–1151. doi: 10.1152/physrev.00031.2020

Table 4.

The good, the bad, and the ugly of modeling studies

“Good” Modeling Studies	“Bad” Modeling Studies
• Use realistic geometries and dimensions collected from experimental datasets. • Account for interspecies and condition differences in physiological parameters and biomechanical properties of tissues. • Appreciate that their conclusions are limited by the assumptions included in the model.Titles state that they are modeling studies when they do not include experimental evidence. • Are primarily hypothesis testing, but acknowledge that they are unable to fully confirm or reject a hypothesis pertaining to a complex biological process and are therefore mainly supportive evidence.Generate hypotheses and provide constructive and insightful conclusions that can be experimentally tested. • Work toward building more complex and realistic models.Include sensitivity testing.	• Use simplified geometries and dimensions not derived from experimental datasets. • Use parameter values from different species, ages, conditions (e.g., awake, sleep, anesthesia, etc.), and live/fixed tissue interchangeably. • Overstate the level of confidence about their conclusions and consider them equal to results from experimental approaches. • Include confirmatory biological conclusions in their study titles, without stating that they are modeling studies despite the absence of experimental evidence. • Draw conclusions about biological phenomena without acknowledging the inherent assumptions of the model. • Are believed sufficient to confirm or reject hypotheses about complex biological phenomena. • Do not include sensitivity testing.

“Good” Modeling Studies

“Bad” Modeling Studies

• Use realistic geometries and dimensions collected from experimental datasets.
• Account for interspecies and condition differences in physiological parameters and biomechanical properties of tissues.
• Appreciate that their conclusions are limited by the assumptions included in the model.Titles state that they are modeling studies when they do not include experimental evidence.
• Are primarily hypothesis testing, but acknowledge that they are unable to fully confirm or reject a hypothesis pertaining to a complex biological process and are therefore mainly supportive evidence.Generate hypotheses and provide constructive and insightful conclusions that can be experimentally tested.
• Work toward building more complex and realistic models.Include sensitivity testing.

• Use simplified geometries and dimensions not derived from experimental datasets.
• Use parameter values from different species, ages, conditions (e.g., awake, sleep, anesthesia, etc.), and live/fixed tissue interchangeably.
• Overstate the level of confidence about their conclusions and consider them equal to results from experimental approaches.
• Include confirmatory biological conclusions in their study titles, without stating that they are modeling studies despite the absence of experimental evidence.
• Draw conclusions about biological phenomena without acknowledging the inherent assumptions of the model.
• Are believed sufficient to confirm or reject hypotheses about complex biological phenomena.
• Do not include sensitivity testing.