Use of a Modified Comprehensive Pain Evaluation Questionnaire: Characteristics and Functional Status of Patients on Entry to a Tertiary Care Pain Clinic
Bibliographic record
Abstract
BACKGROUND: With increasing knowledge of chronic pain, clinicians have attempted to assess chronic pain patients with lengthy assessment tools. OBJECTIVES: To describe the functional and emotional status of patients presenting to a tertiary care pain clinic; to assess the reliability and validity of a diagnostic classification system for chronic pain patients modelled after the Multidimensional Pain Inventory; to provide psychometric data on a modified Comprehensive Pain Evaluation Questionnaire (CPEQ); and to evaluate the relationship between the modified CPEQ construct scores and clusters with Diagnostic and Statistical Manual, Fourth Edition - Text Revision Pain Disorder diagnoses. METHODS: Data on 300 new patients over the course of nine months were collected using standardized assessment procedures plus a modified CPEQ at the Comprehensive Pain Program, Toronto Western Hospital, Toronto, Ontario. RESULTS: Cluster analysis of the modified CPEQ revealed three patient profiles, labelled Adaptive Copers, Dysfunctional, and Interpersonally Distressed, which closely resembled those previously reported. The distribution of modified CPEQ construct T scores across profile subtypes was similar to that previously reported for the original CPEQ. A novel finding was that of a strong relationship between the modified CPEQ clusters and constructs with Diagnostic and Statistical Manual, Fourth Edition - Text Revision Pain Disorder diagnoses. DISCUSSION AND CONCLUSIONS: The CPEQ, either the original or modified version, yields reproducible results consistent with the results of other studies. This technique may usefully classify chronic pain patients, but more work is needed to determine the meaning of the CPEQ clusters, what psychological or biomedical variables are associated with CPEQ constructs or clusters, and whether this instrument may assist in treatment planning or predict response to treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".