Breast pain in a diverse population of breast cancer patients
Bibliographic record
Abstract
Abstract Background Breast cancer is the most common cancer in women. The majority of women with breast cancer present with early stage disease requiring surgical management. Post-operative breast pain has been reported to be anywhere from 25–60%. Racial disparities in cancer treatment-related symptom burden are known and linked to worse treatment outcomes. There is sparse data regarding racial/ethnic differences in breast pain among breast cancer patients. We evaluated the prevalence of breast pain in breast cancer patients and characterized the pain using a modified short-form McGill pain questionnaire in our diverse population. Methods We performed a cross sectional study, including 237 patients from various outpatient oncology clinics and breast cancer survivorship groups on Oahu and Maui. Participants had the option to complete the survey in person at the clinic or online. Results Eight-four respondents (35.4%) reported breast pain. There were no statistical differences seen in breast pain likelihood according to racial/ethnic group. On multivariate analysis, we did however find significant racial/ethnic differences in the amount of breast pain, where Chinese, Native Hawaiian and Mixed Asian participants reported significantly less pain compared to White participants (1.36, 2.16 and 2.22 vs 2.92, p = < 0.0001, 0.03 and 0.05) on a 10-point pain scale. We found differences in breast pain according to age, chemotherapy, radiation therapy and endocrine therapy use as well as survey location. No differences were seen according to the type of breast or axillary surgery. The most common descriptors of breast pain were sensory compared to affective characteristics. The average self-reported pain score found was 3/10. Overall, in women with breast pain, 33.4% reported the breast pain affected their sleep with 16.7% reporting it affected their work and 15.4% reporting it affected 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 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 | 0.000 | 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) | 0.002 | 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".