Original ArticleA Multicenter Study of the Revised Edmonton Staging System for Classifying Cancer Pain in Advanced Cancer Patients
Bibliographic record
Abstract
The comparative analysis of analgesic interventions for cancer pain is greatly compromised by the lack of well-validated and clinically acceptable tools, which allow a composite classification of pain and patient population characteristics. Although the Edmonton Staging System (ESS) for cancer pain was developed for this purpose, clinical and research utility has been limited due to problems associated with the assessment of some items, especially in relation to definitions and terminology. To overcome these limitations, we designed a revised ESS (rESS) and conducted a multicenter study to determine its inter-rater reliability and predictive value. In revising the rESS, we hypothesized that patients with less problematic pain features would require a shorter time to achieve stable pain control, require less complicated analgesic regimens, be more responsive to opioid therapy, and use lower opioid doses. The rESS items include mechanism of pain, presence or absence of incidental pain, presence or absence of psychological distress and addictive behavior, and level of cognitive function. Patients with cancer pain who were consecutively admitted to two different hospice centers, an acute care consultation service in a teaching
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".