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 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 | 0.027 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".