Nonpain Symptom Prevalence and Intensity of Inpatients With Moderate to Severe Cancer Pain in China
Bibliographic record
Abstract
OBJECTIVES: To identify prevalence and severity of nonpain symptoms and to clarify possible influences on each nonpain symptom. METHODS: The study used a descriptive survey design. Chinese version of the Edmonton Symptom Assessment System was used. Patients' demographic and pain characteristics were collected. RESULTS: The most common symptoms reported were loss of appetite (94.3%) followed by insomnia (93.3%), and tiredness (91.6%). Prevalence rates of nonpain symptom were all above 70% except "thinking clearly." Prevalence and severity of nonpain symptoms varied by gender, age, primary cancer, and pain characteristics, especially intensity, number of breakthrough pain episodes per day, and number of pain sites. CONCLUSIONS: Most inpatients with cancer pain experienced concurrent nonpain symptoms. Comprehensive symptom assessment and intervention managing multiple symptoms are essential for these inpatients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".