The routine use of the Edmonton Classification System for Cancer Pain in an outpatient supportive care center
Bibliographic record
Abstract
OBJECTIVE: There is no standardized and universally accepted pain classification system for the assessment and management of cancer pain in both clinical practice and research studies. The Edmonton Classification System for Cancer Pain (ECS-CP) is an assessment tool that has demonstrated value in assessing pain characteristics and response. The purpose of our study was to determine the relationship between negative ECS-CP features and some pain-related variables like pain intensity and opioid use. We also explored whether the number of negative ECS-CP features was associated with higher pain intensity. METHOD: The electronic charts of 100 patients at an outpatient supportive care clinic in a comprehensive cancer center were reviewed for variables like patient characteristics, initial ECS-CP assessment, morphine equivalent daily dose (MEDD), opioid rotation, Edmonton Symptom Assessment Score (ESAS), and use of adjuvant analgesics. RESULTS: Some 91 of the 100 charts were eligible for analysis. The most common primary cancer type was gastrointestinal (22.1%). The median pain intensity was 6, and the median MEDD was 45 mg. Neuropathic pain was associated with higher median pain intensity (7 vs. 5, p = 0.007) and median MEDD requirement (83 vs. 30, p = 0.013). Psychological distress was associated with higher median pain intensity (7 vs. 5, p = 0.042). Incident pain was also associated with a trend toward higher pain intensity (6 vs. 5, p = 0.06). A higher number of negative ECS-CP features was associated with higher pain intensity (p = 0.01). SIGNIFICANCE OF RESULTS: The ECS-CP was successfully completed in the majority of patients, demonstrating its utility in routine clinical practice. Neuropathic pain and psychological distress were associated with higher pain intensity. Also, neuropathic pain was associated with a higher MEDD. A higher sum of negative ECS-CP features was associated with higher pain intensity. Further studies will be needed to verify and explore these observations.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".