Don't you hate it when biased patient populations mess up the sample?
In large academic hospitals, [tumors] come from a wide variety of patients from across different cities, states, or countries. In contrast, local hospitals treat their regional constituencies.
The potential for demographically-biased patient populations and biased [tumor] subsets is a possibility. These trends can reinforce particular treatment strategies at local institutions over time. For example, if patients from a community highly populated by retirees (e.g. southern Florida) presented with a [glioblastoma multiforme], clinicians would be apt to predict that these older patients would likely succumb to their malignancies within one year.
Current treatment for patients diagnosed with high grade gliomas consists of surgical resection followed by toxic and expensive therapy schedules that are minimally effective. But if these elderly patients were suffering from a [ProNeural tumor], they would have a high likelihood for surviving at least two to three years or longer. These patients could then be distinguished from patients who otherwise present identically under the microscope or according to their patient biographical sketch. This would permit time to enroll in potentially beneficial clinical trials.
Thus, if grade and age alone were considered for prognosis, these factors would lead clinicians to prescribe unnecessary treatments due to trends reinforced by regional sampling biases.
Liz comment from 2022: Holy cow! Here’s evidence I was reading the work of Cloughsey, Lai and Mischel before I was even in metaphorical diapers. —Liz
