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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.061 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".