The Edmonton Classification System for Cancer Pain: a tool with potential for an evolving role in cancer pain assessment and management
Bibliographic record
Abstract
Introduction: Undertreatment of cancer pain is associated with inadequate assessment and inconsistent or non-standardized classification, resulting in failure to both appreciate its multidimensional nature and appropriately target therapeutic interventions. This review examines the classification of cancer pain with a focus on the progressive development of the Edmonton Classification System for Cancer Pain (ECS-CP); the appropriateness of its constituent features, associated outcomes and its potential future development in cancer pain classification.Areas covered: A Medline search from 1989 to November 2017, using combined terms ‘cancer’ or ‘oncology’, ‘Edmonton’, ‘pain’ or ‘analgesia’, and ‘staging’ or ‘classification’, identified 280 records. A total of 20 studies with empirical data relating to validation studies of the ECS-CP or evaluation of either its constituent or proposed domains were selected for inclusion in the core review.Expert commentary: The ECS-CP is a tool in evolution and a valid template for further cancer pain classification development. The assessment of ECS-CP domains requires a standardized approach. The domain ratings can inform the therapeutic strategy, and are associated with pain management outcomes, particularly stable pain control. The ECS-CP enables standardized reporting, based on patients’ pain and related characteristics, and thus may improve the validity of comparisons across research study samples.
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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 | 0.004 | 0.000 |
| 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".