Examining Postoperative Pain in South Asian Females with Breast Cancer or Pre-Cancerous Lesions Following Mastectomy or Lumpectomy: A Feasibility Study
Bibliographic record
Abstract
Introduction: South Asian females are diagnosed with breast cancer at a later stage as compared to other Canadian females. This may be due to language barriers and fear of stigma in their community. Unfortunately, this can lead to the need for more extensive surgery. Following surgery, South Asian females experience acute pain that may lead to chronic postoperative pain. Research regarding pain in South Asian females who undergo surgery for breast cancer in Canada is lacking. This study aimed to examine the feasibility of recruiting South Asian females and to examine pain following breast surgery. Methods: This descriptive, observational study examined the feasibility of recruiting and retaining South Asian females with diagnosed breast cancer or pre-cancerous lesions in a study that examines acute pain after mastectomy and lumpectomy. South Asian females were recruited in person or over the telephone from a breast cancer clinic in the Greater Toronto Area. Pain was examined using the numeric rating scale, the BPI-SF, on postoperative day (POD) 1 and 7. Analgesic use and side effects were also examined. Results: Twenty-three of 29 eligible South Asian females consented to participate. The mean age of participants was 58.8 years old (SD 13.3), and most were from India (60.9%). On POD 1 participants reported a mean “worst” pain score of 6.3 out of 10 (SD = 2.4), and by POD 7, the mean pain score decreased to 4.5 (SD = 2.5). Pain interfered with normal work, general activity, and sleep. Conclusions: South Asian females who undergo breast surgery are willing to participate in studies that may help others. Participants reported mild to moderate pain up to 7 days after surgery. Education regarding how to treat pain and analgesic protocols may help this population better manage their pain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".