Patient-Reported Outcomes of Pain and Related Symptoms in Integrative Oncology Practice and Clinical Research: Evidence and Recommendations
Bibliographic record
Abstract
Pain is a primary concern among patients with cancer and cancer survivors. Integrative interventions such as acupuncture, massage, and music therapy are effective nonpharmacologic approaches for cancer pain with low cost and minimal adverse events. Patient-reported outcomes (PROs) that have been validated in many clinical and research settings can be used to evaluate pain intensity, associated symptom burden, and quality of life. Clearly defined, reliable PROs can improve patient satisfaction and symptom control. As integrative oncology continues to evolve and expand, cancer-related pain PROs must be standardized to accurately guide clinicians and researchers. Well-validated pain PROs, such as the Brief Pain Inventory, are among the most commonly used for pain intensity assessment. Multiple symptom assessment tools such as the MD Anderson Symptom Inventory, the Memorial Symptom Assessment Scale, the Edmonton Symptom Assessment System, and the Patient-Reported Outcomes-Common Terminology Criteria for Adverse Events measurement system can also capture pain-associated symptom burden. Electronic PROs provide flexibility in collecting and analyzing PRO data. Clinical trials using carefully selected PROs and rigorous statistical analysis plans are fundamental to conducting high-quality integrative oncology research and promoting utilization of effective integrative interventions to improve patient outcomes. In this review, we aim to summarize current, validated PROs specific to cancer-related pain to aid integrative oncology clinicians and researchers in patient care and in study design and implementation.
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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 | 0.019 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".