The Efficacy of Fat Grafting on Treating Post-Mastectomy Pain with and without Breast Reconstruction: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Post-mastectomy pain syndrome (PMPS), characterized by persistent pain lasting at least three months following mastectomy, affects 20–50% of breast surgery patients, lacking effective treatment options. A review was conducted utilizing EMBASE, MEDLINE, and all evidence-based medicine reviews to evaluate the effect of fat grafting as a treatment option for PMPS from database inception to 29 April 2023 (PROSPERO ID: CRD42023422627). Nine studies and 812 patients in total were included in the review. The overall mean change in visual analog scale (VAS) was −3.6 in 285 patients following fat grafting and 0.5 in 147 control group patients. There was a significant reduction in VAS from baseline in the fat grafting group compared to the control group, n = 395, mean difference = −2.17 (95% CI, −2.95 to −1.39). This significant improvement was also noted in patients who underwent mastectomy without reconstruction. Common complications related to fat grafting include capsular contracture, seroma, hematoma, and infection. Surgeons should consider fat grafting as a treatment option for PMPS. However, future research is needed to substantiate this evidence and to identify timing, volume of fat grafting, and which patient cohort will benefit the most.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".