Intraoperative SPY Reduces Post-mastectomy Skin Flap Complications: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Indocyanine-green and laser-assisted fluorescence angiography, known as the SPY system, is a recently developed tool that has shown promise in assessing tissue perfusion. Its intraoperative use is becoming more common particularly in breast surgery. This systematic review aims to determine whether SPY technology can reduce postoperative complications related to tissue ischemia, specifically skin necrosis of the mastectomy native breast skin flaps. METHODS: A systematic review of the literature was performed based on the PRISMA guideline. All studies that involved use of the SPY system to assess perfusion of postmastectomy skin flaps from January 1, 1960, to March 1, 2018 were included. Postoperative complications, including mastectomy skin flap necrosis were extracted from the selected studies. The perfusion-related complication rates and unexpected reoperation rates across multiple studies were then reviewed. RESULTS: Five relevant articles were identified including 902 patients undergoing mastectomy and native breast flap reconstructive procedures. Groups that used indocyanine-green angiography had statistically less incidence of native breast skin flap necrosis and unexpected reoperations due to perfusion-related complications compared with groups that monitored flaps with only clinical observation (odds ratio 0.54 for skin necrosis, and 0.36 for reoperation). CONCLUSIONS: In this systematic review, the incidence of native breast skin flap necrosis and unexpected reoperations were found to be statistically lower in cases where SPY was used. However, more prospective studies are required to establish SPY angiography as an accurate and cost-effective tool for assessment of tissue perfusion.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.003 | 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".