A cross-sectional study of breast pain in a diverse population of breast cancer patients
Bibliographic record
Abstract
BACKGROUND: Breast cancer is the most common cancer in women, and postoperative breast pain has been reported to be anywhere from 25% to 60%. However, there is sparse data regarding racial/ethnic differences in breast pain among breast cancer patients. METHODS: We performed a cross-sectional anonymous survey of breast cancer patients from the Hawaii Cancer Consortium over a 6-week period between 2019 and 2020. The 237 breast cancer participants were ages 29 to 98, 74% Asian, and mainly from outpatient oncology clinics. We evaluated the prevalence of breast pain in a diverse group of breast cancer patients and characterized the pain using a modified short-form McGill pain questionnaire (sfMPQ). RESULTS: Eighty-fourrespondents(35.4%) reported breast pain. On univariable analysis, we found significant racial/ethnic differences in the amount of breast pain, where Chinese and Japanese participants reported significantly less pain compared to White participants on a 10-point pain scale. We found differences in breast pain according to age and endocrine therapy use as well as survey location, however, no differences were seen according to chemotherapy, radiation, or breast surgery. Based on the sfMPQ, the most common descriptors of breast pain were sensory (throbbing, shooting, and stabbing) compared to affective (tiring-exhausting, sickening, fearful, and punishing-cruel) characteristics. Although they were described as mild and intermittent, in women with breast pain, 33.4% reported the breast pain affected their sleep, 16.7% their work, and 15.4% their sexual activity. CONCLUSIONS: Breast pain is a significant problem in our breast cancer community. This survey assessment has informed our understanding of breast pain in our diverse population. In turn, we are developing culturally appropriate pain management strategies to treat this challenging symptom common in breast cancer survivors.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".