Composite Pain Index: Reliability, Validity, and Sensitivity of a Patient-Reported Outcome for Research
Bibliographic record
Abstract
OBJECTIVE: A single score that represents the multidimensionality of pain would be an innovation for patient-reported outcomes. Our aim was to determine the reliability, validity, and sensitivity of the Composite Pain Index (CPI). DESIGN: Methodological analysis of data from a randomized controlled, pretest/post-test education-based intervention study. SETTING: The study was conducted in outpatient oncology clinics. SUBJECTS: The 176 subjects had pain, were 52 ± 12.5 years on average, 63% were female, and 46% had stage IV cancers. METHODS: We generated the CPI from pain location, intensity, quality, and pattern scores measured with an electronic version of Melzack's McGill Pain Questionnaire. RESULTS: The internal consistency values for the individual scores comprising the CPI were adequate (0.71 baseline, 0.69 post-test). Principal components analysis extracted one factor with an eigenvalue of 2.17 with explained variance of 54% at baseline and replicated the one factor with an eigenvalue of 2.11 at post-test. The factor loadings for location, intensity, quality, and pattern were 0.65, 0.71, 0.85, and 0.71, respectively (baseline), and 0.59, 0.81, 0.84, and 0.63, respectively (post-test). The CPI was sensitive to an education intervention effect. CONCLUSIONS: Findings support the CPI as a score that integrates the multidimensional pain experience in people with cancer. It could be used as a patient-reported outcome measure to quantify the complexity of pain in clinical research and population studies of cancer pain and studied for relevance in other pain populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".