The effect of post mastectomy radiation therapy on breast reconstruction with and without acellular dermal matrix: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
BACKGROUND: The widespread implementation of acellular dermal matrix (ADM) has broadened the reconstructive repertoire for alloplastic breast reconstruction. ADM's role in the context of postoperative radiation therapy remains unclear. The present review will evaluate whether ADM reduces complication rates in patients undergoing post-mastectomy radiation therapy (PMRT). METHODS: A healthcare librarian assisted in performing a search strategy of electronic databases MEDLINE (via Ovid), EMBASE, and CENTRAL. A combination of the keywords and Medical Subject Headings (MESH) to describe the various commercially available ADMs and terms for radiation therapy will be used. The search strategy will identify patients undergoing postoperative radiation following implant-based breast reconstruction and compare outcomes between those with and without ADM. Extracted data will include patient demographics, intraoperative data, and postoperative complications. Data on patient satisfaction and resource utilization will also be extracted if available. The references of selected works will be reviewed for additional studies meeting study criteria. Only peer-reviewed papers written in English will be included. The study data will be assessed for risk of bias and heterogeneity. Providing that sufficient studies can be identified, a meta-analysis will be performed. This review has been registered with PROSPERO (CRD42017056495). CONCLUSIONS: To date, the short- and long-term performance of ADM in the context of postoperative radiation remains unclear. The objective of the present review will be to critically evaluate the literature with the intention of improving postoperative outcomes in the context of mastectomy and radiation.
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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 | 0.036 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.028 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.002 |
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".