  Modern medical diagnosis relies on precise pain assessment tools in
translating clinical information from patient to physician. The McGill Pain
Questionnaire (MPQ) is a clinical pain assessment technique that utilizes 78
adjectives of different intensities in 20 different categories to quantity a
patient's pain. The questionnaire's efficacy depends on a predictable pattern
of adjective use by patients experiencing pain. In this study, I recreate the
MPQ's adjective intensity orderings using data gathered from patient forums and
modern NLP techniques. I extract adjective intensity relationships by searching
for key linguistic contexts, and then combine the relationship information to
form robust adjective scales. Of 17 adjective relationships predicted by this
research, only 4 diverge from the MPQ's orderings, which is statistically
significant at the 0.1 alpha level. The results suggest predictable patterns of
adjective use by people experiencing pain, but call into question the MPQ's
categories for grouping adjectives.
