What is stable pain control? A prospective longitudinal study to assess the clinical value of a personalized pain goal
Bibliographic record
Abstract
BACKGROUND: A universal consensus regarding standardized pain outcomes does not exist. The personalized pain goal has been suggested as a clinically relevant outcome measure. AIM: To assess the feasibility of obtaining a personalized pain goal and to compare a clinically based personalized pain goal definition versus a research-based study definition for stable pain. DESIGN: Prospective longitudinal descriptive study. MEASURES: The attending physician completed routine assessments, including a personalized pain goal and the Edmonton Classification System for Cancer Pain, and followed patients daily until stable pain control, death, or discharge. Stable pain for cognitively intact patients was defined as pain intensity less than or equal to desired pain intensity goal (personalized pain goal definition) or pain intensity ⩽3 (Edmonton Classification System for Cancer Pain study definition) for three consecutive days with <3 breakthroughs per day. SETTING/PARTICIPANTS: A total of 300 consecutive advanced cancer patients were recruited from two acute care hospitals and a tertiary palliative care unit. RESULTS: In all, 231/300 patients (77%) had a pain syndrome; 169/231 (73%) provided a personalized pain goal, with 113/169 (67%) reporting a personalized pain goal ⩽3 (median = 3, range = 0-10). Using the personalized pain goal definition as the gold standard, sensitivity and specificity of the Edmonton Classification System for Cancer Pain definition were 71.3% and 98.5%, respectively. For mild (0-3), moderate (4-6), and severe (7-10) pain, the highest sensitivity was for moderate pain (90.5%), with high specificity across all three categories (95%-100%). CONCLUSION: The personalized pain goal is a feasible outcome measure for cognitively intact patients. The Edmonton Classification System for Cancer Pain definition closely resembles patient-reported personalized pain goals for stable pain and would be appropriate for research purposes. For clinical pain management, it would be important to include the personalized pain goal as standard practice.
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 | 0.005 | 0.011 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".