Tipping the Scales: A Corpus-Based Reconstruction of Adjective Scales in the McGill Pain Questionnaire

TL;DR

This study reconstructs the adjective intensity scales of the McGill Pain Questionnaire using NLP analysis of patient forum data, confirming most of its orderings but questioning some category groupings.

Contribution

It introduces a corpus-based method to empirically verify and refine the adjective scales used in pain assessment questionnaires.

Findings

17 adjective relationships predicted, 4 diverge from MPQ orderings

Statistical significance at the 0.1 alpha level for divergence

Predictable adjective use patterns observed in patient language

Abstract

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.