Incidence of breast pain in breast cancer patients in a multi-ethnic cohort.
Bibliographic record
Abstract
e24085 Background: Breast cancer is the most common cancer in women. Prevalence rates for persistent pain following breast cancer surgery are reported to be up to 60%. Younger age, radiation, more invasive surgery, and acute post-operative pain have been identified as predictors of chronic pain after surgery. Several studies have looked at factors predicting breast pain, but to our knowledge none of these studies have reported on perceived pain among ethnic groups beyond white and non-white comparisons. Methods: Participants were asked to complete an anonymous breast pain questionnaire based on the McGill pain questionnaire, either online or face-to-face in a clinical setting. Incidence and type of breast pain, common risk factors (age, type of surgery, treatment: chemotherapy, radiation and endocrine therapy), and race/ethnicity was collected and analyzed through descriptive and multivariate analysis. Results: 238 responses were collected and analyzed. About 36% of participants reported breast pain, where 82% reported these symptoms for more than a year. More than 71% identified as non-white, with the majority identifying as Asian (50%) followed by White (11%), Multi-ethnic (9%) and Native Hawaiian (8%). The majority of participants were older than 60 years of age (57%), with 30% being older than 70. Japanese, Filipino and Native Hawaiian participants reported significantly more pain compared to White participants (p < .0001). The majority of participants reported a 3/10 pain level on a pain scale and described overall breast pain as mild. The most common descriptor of mild pain was aching and represents the dullness pain characteristic. The most common descriptor of moderate pain was sharp characterizing an incisive pressure, and the most common descriptors of severe pain were heavy, tender, shooting and throbbing, representing various pain characteristics. Participants who were undergoing radiation (p < .0001) or chemotherapy (p < .05) reported statistically higher breast pain, although there were no differences in breast pain according to the type of surgery (mastectomy vs lumpectomy). Participants who completed the survey online also reported more pain (p < .0001) than participants who completed the survey face-to-face. Conclusions: Breast pain is a significant problem in our breast cancer community. This questionnaire has informed our understanding of the type of pain our multi-ethnic breast cancer patients are experiencing and in turn we are developing culturally appropriate pain management strategies to treat this challenging symptom for 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".