Edmonton Classification System for Cancer Pain: Comparison of Pain Classification Features and Pain Intensity across Diverse Palliative Care Settings in Canada
Bibliographic record
Abstract
Background: The goal of the Edmonton Classification System for Cancer Pain (ECS-CP) is to create an international classification system for cancer pain. Previous studies reinforce the need for standardized training to ensure consistency across assessors. There is no universally accepted classification for neuropathic pain. Objectives: Our primary objective was to describe the prevalence of ECS-CP features in a diverse sample of advanced cancer patients, using assessors with standardized training. The secondary objectives were to: (1) determine the prevalence of neuropathic pain using the Neuropathic Pain Special Interest Group (NeuPSIG) criteria and (2) examine the relationship between specific predictors: ECS-CP features, age, Palliative Performance Scale, Morphine Equivalent Daily Dose (MEDD), setting, and pain intensity; and neuropathic pain. Methods: A total of 1050 adult patients with advanced cancer were recruited from 11 Canadian sites. A clinician completed the ECS-CP and NeuPSIG criteria, and collected additional information including demographics and pain intensity (now). All assessors received standardized training. Results: Of 1050 evaluable patients, 910 (87%) had cancer pain: nociceptive ( n = 626; 68.8%); neuropathic ( n = 227; 24.9%); incident ( n = 329; 36.2%); psychological distress ( n = 209; 23%); addictive behavior ( n = 51; 5.6%); and normal cognition ( n = 639; 70.2%). The frequencies of ECS-CP features and pain intensity scores varied across sites and settings, with more acute settings having higher frequencies of complex pain features. The overall frequency of neuropathic pain was 24.9%, ranging from 11% (hospices) to 34.2% (palliative outpatient clinic) across settings. Multivariate logistic regression analysis revealed that age <60 years, MEDD ≥19 mg, pain intensity ≥7/10, and incident pain were significant independent predictors of neuropathic pain ( p < 0.05). Conclusion: The ECS-CP was able to detect salient pain features across settings. Furthermore, the frequencies of neuropathic pain utilizing the NeuPSIG criteria fits within the lower-end of literature estimates (13%–40%). Further research is warranted to validate the NeuPSIG criteria in cancer pain.
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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 | 0.002 |
| 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 | 0.001 |
| 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".