Characteristics and Perioperative Risk Factors for Persistent Pain after Breast Cancer Surgery: A Prospective Cohort Study
Bibliographic record
Abstract
Objective: Persistent pain is a common complication after breast cancer surgery. We sought to determine the characteristics of persistent pain after breast cancer surgery and identify perioperative risk factors associated with its development. Methods: This prospective cohort study uses data from a prior randomized controlled trial of 100 patients undergoing breast cancer surgery. Patients were assessed on the presence and characteristics of pain at 3 months after surgery. Baseline and perioperative data were explored for potential associations with persistent pain in univariate and multivariate logistic regression models. Results: Fifty-three percent of patients reported persistent pain 3-months after surgery. Pain was primarily located in the axilla, chest, and shoulder, with the vast majority of patients with pain (96.2%) reporting a neuropathic pain feature. The mean intensity of pain was 2.5 (standard deviation [SD] 2.4, on a 0 to 10 pain scale) and persistent pain was associated with worse quality of life scores (p = 0.004) and increased use of analgesics (p = 0.015). Variables found to be associated with persistent pain in our univariable and multivariable-adjusted analyses were preoperative employment (OR 2.70, 95% CI 1.04–9.66, p = 0.042), acute postoperative pain during movement (OR 1.63, 95% CI 1.06–2.51, p = 0.027), and adjuvant chemotherapy (OR 3.30, 95% CI 1.19 to 9.15, p = 0.022). Conclusions: Persistent pain after breast cancer surgery is neuropathic and is associated with reduced quality of life and increased analgesic need. Future research should focus on perioperative interventions to reduce acute postoperative pain and consideration of modified adjuvant chemotherapy regimens to address modifiable risk factors and potentially reduce the incidence of persistent pain after breast cancer surgery.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".