The Edmonton Classification System for Cancer Pain in Patients with Bone Metastasis: a descriptive cohort study
Bibliographic record
Abstract
PURPOSE: We describe the prevalence of the Edmonton Classification System for Cancer Pain (ECS-CP) features in patients with bone metastasis and cancer-induced bone pain (CIBP) and the relationship between ECS-CP features, pain intensity, and opioid consumption. METHODS: We assessed ECS-CP features and recoded pain mechanisms and opioid use in adult patients with bone metastasis. Validated measures were used to assess pain intensity, incident pain, psychological distress, addictive behavior, and cognition. RESULTS: Among 147 eligible patients, 95.2% completed the assessment. Mean participant age was 73.2 years, the majority female (52.1%) with breast cancer occurring most commonly (25.7%). One or more ECS-CP features were present in 96.4% and CIBP in 75.7% of patients. The median average and worst pain scores were 3 and 6, respectively. Neuropathic pain was the most prevalent pain mechanism (45.0%) and was associated with breakthrough pain frequency (p=0.014). Three-quarters had incident pain, which was strongly associated with a higher average and worst pain scores (3.5 and 7, p<0.001 for both), background oral morphine equivalent daily dose (26.7mg, p=0.005), and frequency of daily breakthrough analgesia (1.7 doses/day, p=0.007). Psychological distress (n=90, 64.3%) was associated with a significantly higher average pain score (4, p=0.009) and a slightly higher worst pain score (7, p=0.054). Addictive behaviour and cognitive dysfunction were relatively uncommon (18.6% and 12.9%, respectively). CONCLUSION: There is a need to promote standardized assessment and classification of pain syndromes such as CIBP. The ECS-CP may allow us to consider CIBP in a systematic manner and develop personalized pain interventions appropriate to the pain profile. TRIAL REGISTRATION: Retrospectively registered in ANZCTR ACTRN12622000853741 (16/06/2022).
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".