How do we measure the adequacy of cancer pain management? Testing the performance of 4 commonly used measures and steps towards measurement refinement
Bibliographic record
Abstract
Abstract Although pain is the most common and disabling cancer symptom requiring management, the best index of cancer pain management adequacy is unknown. While the Pain Management Index is most commonly used, other indices have included relief, satisfaction, and pain intensity. We evaluated their correlations and agreement, compared their biopsychosocial correlates, and investigated whether they represented a single construct reflecting the adequacy of cancer pain management in 269 people with advanced cancer and pain. Despite moderate-to-severe average pain in 52.8% of participants, 85.1% had PMI scores suggesting adequate analgesia, pain relief was moderate and satisfaction was high. Correlations and agreement were low-to-moderate, suggesting low construct validity. Although the correlates of pain management adequacy were multidimensional, including lower pain interference, neuropathic and nociceptive pain, and catastrophizing, shorter cancer duration, and greater physical symptoms, no single index captured this multidimensionality. Principal component analysis demonstrated a single underlying construct, thus we constructed the Adequacy of Cancer Pain Management from factor loadings. It had somewhat better agreement, however correlates were limited to pain interference and neuropathic pain. This study demonstrates the psychometric shortcomings of commonly used indices. We provide suggestions for future research to improve measurement, a critical step in optimizing cancer pain management. Perspective The Pain Management Index and other commonly used indices of cancer pain management adequacy have poor construct validity. This study provides suggestions to improve the measurement of the adequacy of cancer pain management.
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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 | 0.142 | 0.285 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".