Self-Perceived Pain in Chinese Patients With Cancer
Bibliographic record
Abstract
BACKGROUND: Pain is one of the most burdensome and prevalent symptoms cancer patient report and it has severe negative impact on patient's quality of life. The aim of this study is to estimate the prevalence of pain and to test the association between demographic, clinical, psychological factors, and self-assessed pain in Chinese cancer population. METHODS: A total of 553 cancer patients were recruited in this cross-sectional study. Patient's basic demographic data was collected by a study-designed information sheet, and patient's pain, sleep disturbance and psychological distress were assessed by several validated measurements (MPQ-SF, AIS, FoP-Q-SF, PHQ-9, and GAD-7). Descriptive statistics and hierarchical multiple regression analyses were performed. RESULTS: Of the 553 patients, 411 (74.32%) patients reported that they experience some degree of pain. Fear of progression, anxiety, insomnia, and depressive symptoms were significantly associated with different subscales and the overall pain score in bivariate correlation matrix. Insomnia, depressive symptoms, and fear of cancer progression were significant independent factors of cancer pain on multivariate analyses. CONCLUSION: Psychological factors play a great role in the relationship between objective pathophysiology and patient's subjective experience of pain. It is important to evaluate each individual in detail with respect to psychological distress and pain severity when planning treatment and rehabilitation.
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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 | 0.001 | 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) | 0.001 | 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".