P-49 Cancer induced bone pain – the use of edmonton classification system for cancer pain and management of refractory pain
Bibliographic record
Abstract
Purpose Cancer-induced bone pain (CIBP) affects a patient’s functional capacity and quality of life, but there is limited evidence to guide opioid choice. We described the prevalence of the Edmonton Classification System for Cancer Pain (ECSCP) features in patients with bone metastases and assessed the possible efficacy of methadone rotation (MR) compared to other opioid rotations (OOR) in the cohort with refractory pain. Methods Adults with bone metastases were assessed against ECS-CP features and those with worst pain intensity ≥4/10 and/or opioid toxicity graded ≥2 on the Common Terminology Criteria for Adverse Events (CTCAE) were randomised 1:1 to methadone or another opioid rotation. Standardised assessment tools were used at pre-defined study time points up to fourteen days. Results From 147 eligible participants, 92.5% completed assessment. Mean participant age was 73.2 years. One or more ECS-CP features were present in 96.4% and CIBP in 75.7% of patients. Neuropathic pain was the most prevalent pain mechanism (45%) and was associated with breakthrough pain frequency, higher pain scores and background oral morphine equivalent daily dose (OMEDD). Of those with refractory CIBP, 38 (74.5%) consented, and 29 (76.3%, MR: 14, OOR: 15) completed the study. Both groups displayed significant reduction in pain intensities and total pain interference score. OMEDD reduced significantly in MR compared to OOR group (d= -0.8, p=0.05). There was no within group or between group differences in satisfaction with analgesia at the end of the study. Conclusion Standardised assessment and classification of pain syndromes can assist in developing personalized pain interventions. This pilot study demonstrated that MR and OOR in patients with refractory CIBP are feasible, safe, and acceptable to patients. Appropriately powered multi-centre randomised controlled studies are needed to confirm the efficacy of MR and OOR in this cohort.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".