Updates on the preventions and management of post-mastectomy pain syndrome beyond medical treatment: a comprehensive narrative review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: With the significant advances in breast cancer treatment, the survival rates have improved. Consequently, improving the quality of life for breast cancer survivors has emerged an important issue. In this study, we examined the management of post-mastectomy pain syndrome (PMPS) in breast cancer patients thorough a comprehensive literature review. We introduce the preventive measures and pharmacotherapy for PMPS in breast cancer patients and discuss the effectiveness of psychosocial interventions. METHODS: We conducted a literature search for relevant articles in Medline ALL, Cochrane Database of Systematic Reviews, Cochrane CENTRAL, Embase, and nine other databases from October 2023 to January 2024. Chronic pain was defined as pain persisting for more than 3 months after breast cancer surgery. The search included terms related to PMPS, psychological interventions, and breast cancer. Data extraction was done independently by two reviewers, and any discrepancies will be discussed to ensure consensus or by a third reviewer. KEY CONTENT AND FINDINGS: Studies have investigated surgical anesthetics, postoperative medications, and surgical procedures for PMPS prevention, but few have focused on treatment. Our literature search about the usefulness of psychosocial interventions yielded two articles, one was about the usefulness of mindfulness and the other was about the efficacy of yoga. CONCLUSIONS: Mindfulness and yoga show potential efficacy for PMPS treatment, but the evidence is limited. More research is needed to confirm these findings and to explore other psychosocial interventions.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".