Patient-Reported Factors Alleviating Pain Among Persons With Cancer
Bibliographic record
Abstract
Abstract Pain impacts wellbeing and is among the most common symptoms of cancer. Factors that decrease pain severity have been understudied despite their importance for high-quality cancer care. The study purpose was to describe pain alleviating factors and their association with type of cancer. This secondary comparative analysis included 579 participants from studies of inpatients and outpatients with cancer (mean age=58.7±12.3; 27.3% female; 85.5% White, 5.7% Black, 7.6% Other). They completed the McGill Pain Questionnaire on paper or a tablet computer. To determine factors that alleviated pain, we focused on the open-ended question: 1) What kinds of things relieve your pain? We coded text responses into six outcome categories: 1) Activity level, 2) Cognitive, 3) Environmental, 4) Medical, 5) Physical, and 6) Sedentary behavior. We counted the number of activities/factors in each category and conducted multivariable regression analysis adjusting for sociodemographic constructs. Adjusted models revealed that activity (ρ=0.02), cognition (ρ<0.001) and medication (ρ<0.001) were more often endorsed as alleviating factors among individuals living with lung cancer compared to head and neck cancer participants. Those diagnosed with lung cancer (ρ=0.02) and males (ρ=0.02) utilized significantly less physical alleviating factors than head and neck cancer individuals and females. This is the first study to examine pain-alleviating factors among individuals living with cancer. These findings contribute new information regarding activities that alleviate pain among cancer survivors. These findings could inform interventions to promote safe, personalized care designed to alleviate 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".