A validation study of a pain classification system for advanced cancer patients using content experts: the Edmonton Classification System for Cancer Pain
Bibliographic record
Abstract
The purpose of this study was to gather construct validity evidence for a pain classification system for advanced cancer patients using content experts. Two expert panels, representing regional (Panel A, n = 18) and national/international (Panel B, n = 52) palliative medicine and pain specialists, were purposefully selected to participate in a modified Delphi survey technique, to evaluate an existing pain classification system, the Revised Edmonton Staging System (rESS). Each panel participated in two survey rounds, with response rates of 67% (Panel A, Round 1), 39% (Panel A, Round 2), 56% (Panel B, Round 1) and 64% (Panel B, Round 2). The rESS consists of five features: mechanism of pain, incidental pain, psychological distress, addictive behavior and cognitive function. Most participants either agreed or strongly agreed with including the five existing rESS features in a pain classification system, ranging from 67% (Panel A, cognitive function) to 100% (Panel B, mechanism of pain). Most participants suggested keeping the current definitions for these features, with some revisions. Based on participant feedback, definitions for incidental pain, psychological distress, addictive behavior and cognitive function were revised, including the development of guidelines for use. To reflect its intended use as a classification system, the name of the instrument was changed to the Edmonton Classification System for Cancer Pain (ECS-CP).
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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 | 0.092 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".