Risk factors related to chronic pain after breast cancer surgery: a prospective cohort study
Bibliographic record
Abstract
Aim: Chronic pain after breast cancer surgery (CPBCS) is a significant clinical problem affecting 13% to 93% of patients. Furthermore, 5% to 10% of CPBCS patients are estimated to suffer from severe and disabling CPBCS. Thus, the aim of this prospective cohort study was to identify pre-, intra- and post-operative factors related to CPBCS risk and intensity at three months follow up. Methods: Ninety-five female patients scheduled to undergo breast cancer surgery were recruited from the Jewish General Hospital, Montreal, Quebec. Baseline data was collected on age, pre-operative pain, anxiety, and depression. Telephone follow-up interviews were conducted at seven days and three months after surgery to assess the acute pain and CPBCS, respectively, using the brief pain inventory scale. Intra-operative data on type of surgery, axillary status, radiotherapy and chemotherapy was assessed from physicians' charts. Multivariable logistic regression and linear regression analyses were used to assess factors for CPBCS risk and CPBCS intensity, respectively, at three months follow-up. Results: Eighty-two patients completed the three months follow-up. From those, 45 (55%) reported CPBCS, and 24 patients (53.33%) reported moderate pain (NRS 3–7). In the multivariable analyses only pre-operative pain (odds ratio (OR) = 4.41, p = 0.03) increased the CPBCS risk at three months after surgery. CPBCS intensity at three months after surgery was positively related to depression (β = 1.55; p = 0.0005), and chemotherapy (β = 1.34; p = 0.006). Conclusion: Our results demonstrate that pre-operative pain increases the risk of CPBCS. Depression and chemotherapy were associated with CPBCS intensity. These factors should therefore be considered important to be evaluated and managed for the breast cancer surgery patient, in order to reduce the burden of CPBCS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".