Differences in pain, symptom burden, and opioid use among cancer pain patients with active versus non-active disease.
Bibliographic record
Abstract
e20568 Background: Many cancer survivors with non-active disease suffer with pain and other symptoms. This study investigated differences in symptoms and opioid use among cancer pain patients with active versus non-active disease. Methods: Data were obtained from 518 consecutive new patients seen at the Pain Management Center of MD Anderson Cancer Center from 01/01/09 to 06/30/09. Measures: Usual pain was rated on the Brief Pain Inventory. The Edmonton Symptom Assessment Scale (ESAS) was used for ratings of fatigue, shortness of breath, poor appetite, depression, anxiety, drowsiness, difficulty thinking clearly and insomnia. Opioid use was calculated in morphine equivalency daily dose (MEDD) milligrams based on the sum of long- and short-acting opioids used per day. Analyses of Data: Independent samples t-tests were used to make comparisons between patients with active versus non-active disease on continuous variables. Chi-square tests were used to make comparisons across disease status on categorical variables. Results: 349 patients had active disease; 169 patients had non-active disease. Patients with active disease received significantly higher MEDD (125.6 ± 158.8 mg) versus patients with non-active disease (74.4 ± 87.0 mg). Patients with active disease reported significantly higher mean scores on fatigue, poor appetite, and drowsiness. Average weekly pain scores were comparable and moderately high for both groups of patients. Other symptoms and clinical characteristics were not significantly different across disease status. Conclusions: Plausible explanations for the higher opioid use and symptom burden among patients with active disease are cancer treatments and disease progression. A higher level of pain medication is often needed to achieve pain management during active treatment or following recent surgery. The finding of higher fatigue, poor appetite, and drowsiness among those with active disease is also consistent with the symptom burden expected from treatment. Although patients with active disease have a greater symptom burden and need for pain medication, there is a need for pain and symptom management among patients in the non-active disease phase of survivorship.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".