Which variables are associated with pain intensity and treatment response in advanced cancer patients?— Implications for a future classification system for cancer pain
Bibliographic record
Abstract
BACKGROUND: This study is part of a research program to reach consensus on an international cancer pain classification system. A confirmative and explorative approach was applied to investigate which of the variables identified in the literature, by experts and patients that are associated with pain. METHODS: Data from an international, multicentre, cross-sectional study of cancer patients treated with opioids were investigated. Dependent variables were: average pain, worst pain, and pain relief (11-point Numerical Rating Scales). Forty-six independent variables were chosen based upon previous studies. Bivariate analyses identified independent variables associated with at least one of the dependent ones; 21 were included in multivariate linear regression analyses. RESULTS: Two thousand two hundred and seventy-eight patients were investigated; 52% males, mean age 62 years, mean Karnofsky Performance Status 59%, mean daily opioid oral equivalent dose 341 mg. Fifty-eight percent had breakthrough pain. Mean pain scores were: average pain 3.5, worst pain 5.3 and pain relief 74%. Variables most strongly associated with these three dependent variables were: breakthrough pain, psychological distress, sleep, and opioid dose. CONCLUSIONS: Breakthrough pain and psychological distress were confirmed as key variables of a future classification system. Candidate variables were: sleep, opioid dose, pain mechanism, use of non-opioids, pain localisation, cancer diagnosis, location of metastases, and addiction.
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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 | 0.010 | 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".