Relationship between Pain Scores and EORTC QLQ-C15-PAL Scores in Outpatients with Cancer Pain Receiving Opioid Therapy
Bibliographic record
Abstract
Cancer pain is one of the most frequent and distressing symptoms associated with cancer and has a serious impact on the QOL of patients. However, inadequate pain treatment has also been reported in outpatients with cancer pain. The aims of this study were (1) to evaluate the relationship between pain intensity using the Numerical Rating Scale (NRS) and QOL scores using the Japanese version of the European Organization for Research and Treatment of Cancer (QOL Questionnaire Core 15 for Palliative Care (QLQ-C15-PAL)), and (2) to investigate their association with various pain patterns, especially with baseline and breakthrough pain. Forty outpatients who were receiving opioid therapy and obtained informed consent participated. We collected a total of 222 pharmacist consultations during the study period. Global QOL scores and pain scores (PA) in the QLQ-C15-PAL (PA score, 0-100) at the first visit were significantly correlated with worst pain intensity. In addition, the scores for the worst pain were significantly correlated with not only physical functioning scores but also with emotional functioning scores. The correlations between the worst pain NRS and PA scores were positive. Specifically, patients tended to report large variability of NRS scores when the PA score was less than 40 and also when they exhibited pain patterns with "baseline and breakthrough cancer pain in the same day" or "baseline pain throughout the day." Reducing the worst pain NRS and relieving breakthrough pain appear to be important measures to improve the QOL of outpatients receiving opioid therapy for cancer pain.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".