Evaluating the prognostic effect of a pain classification system in patients with advanced cancer.
Bibliographic record
Abstract
95 Background: The Edmonton Classification System for Cancer Pain (ECS-CP) has been shown to predict pain management complexity based on five features: pain mechanism, incident pain, psychological distress, addictive behavior, and cognitive function. The main objective of our study was to explore the association between increasing sum of negative ECS-CP features and achievement of good pain control at first follow up visit at an outpatient palliative care clinic. Methods: Initial and follow up clinical information of 409 eligible supportive care outpatients such as patient demographics, ECS–CP assessment, morphine equivalent daily dose (MEDD), opioid rotation, Edmonton Symptom Assessment Score (ESAS), and personalized pain goal (PPG) were retrospectively reviewed and analyzed. Results: Between the initial consultation and the first follow up visit, the median MEDD requirement increased from 30mg/day to 45mg/day (p < 0.0001) and median pain intensity improved from 6 to 4 (p < 0.0001). Increasing sum of negative ECS–CP features was associated with higher MEDD at consultation, with an increase from 30mg/day with no negative features to 40mg/day with ≥2 negative features (p = 0.046). There was no significant association between increasing sum of negative ECS-CP features and achievement of pain control at follow up visit (0.991, 95% CI: 0.747 – 1.304, p = 0.948). Conclusions: Increasing sum of negative ECS-CP features was associated with higher MEDD at referral but was not predictive of pain control at the follow up visit when pain was managed by a palliative medicine specialist. Further research is needed to further 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 | 0.001 | 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) | 0.002 | 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".