The Edmonton Classification System for Cancer Pain: Comparison of Pain Classification Features and Pain Intensity Across Diverse Palliative Care Settings in Eight Countries
Bibliographic record
Abstract
BACKGROUND: Standardized approaches for assessing and classifying cancer pain are required to improve treatment of patients with complex pain profiles. The Edmonton Classification System for Cancer Pain (ECS-CP) offers a starting point for the evolution of a standardized international classification system for cancer pain and was introduced into multisite research initiatives of the European Palliative Care Research Collaborative (EPCRC). OBJECTIVES: The primary purpose of this study was to describe the prevalence of the five ECS-CP pain classification features: pain mechanism, incident pain, psychological distress, addictive behavior, and cognition--in a diverse international sample of patients with advanced cancer. METHODS: A total of 1070 adult patients with advanced cancer were recruited from 17 sites in Norway, the United Kingdom, Austria, Germany, Switzerland, Italy, Canada, and Australia; 1051 of 1070 patients were evaluable. A clinician completed the ECS-CP for each enrolled patient. Additional information, including pain intensity, were also collected through patient self-reports, using touch-sensitive computers. RESULTS: Of 1051 evaluable patients, 670 (64%) were assessed by a clinician as having cancer pain: nociceptive pain (n=534; 79.7%); neuropathic pain (n=113; 16.9%); incident pain (n=408; 60.9%); psychological distress (n=212; 31.6%); addictive behavior (n=30; 4.5%); normal cognition (n=616; 91.9%). The prevalence of ECS-CP features and pain intensity scores (11-item scale; 0=none, 10=worst; rated as now) varied substantially across sites and locations of care. CONCLUSION: The ECS-CP is a clinically relevant systematic framework, which is able to detect differences in salient pain classification features across diverse settings and countries. Further validation studies need to be conducted in varied advanced cancer and palliative care settings to advance the development of the ECS-CP toward an internationally recognized pain classification system.
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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 | 0.006 | 0.003 |
| 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 | 0.000 |
| 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".