Optimal timing of postmastectomy radiotherapy in two-stage prosthetic breast reconstruction: An updated meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is no consensus on the timing of postmastectomy radiotherapy (PMRT) in relation to the exchange procedure in breast cancer patients undergoing the immediate two-stage prosthetic breast reconstruction. This meta-analysis investigated the reconstruction failure, complications, and cosmesis between PMRT to the tissue expander (TE) and PMRT to the permanent implant (PI). METHODS: A literature search was conducted in PubMed and Embase databases until February 2022. Studies presenting at least one aspect relating to reconstruction failure, complications, and cosmesis between two cohorts were included. Newcastle-Ottawa Scale (NOS) was used to assess the risk of bias in included studies. RESULTS: Eleven studies presenting 1447 patients were enrolled. Three studies were prospective controlled research. The risk for implant loss was higher in PMRT to TE cohort (RR 1.75; 95% CI, 1.03 to 2.98; p = 0.04); meanwhile, the PMRT to TE cohort had a significantly lower risk of capsular contracture (RR 0.47; 95% CI, 0.29 to 0.78; p = 0.003). However, the synthesized result should be interpreted sensibly due to heterogeneity in statistical methods and definitions. CONCLUSION: Delivering PMRT to PI may reduce the risk of implant loss, while delivering PMRT to TE can reduce the risk of severe capsular contracture. More high-quality studies are warranted for the refinement of clinical practice.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.060 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".