Indocyanine green-based fluorescent angiography in breast reconstruction.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Fluorescent angiography (FA) has been useful for assessing blood flow and assessing tissue perfusion in ophthalmology and other surgical disciplines for decades. In plastic surgery, indocyanine green (ICG) dye-based FA is a relatively novel imaging technology with high potential in various applications. We review the various FA detector systems currently available and critically appraise its utility in breast reconstruction. METHODS: A review of the published English literature dating from 1950 to 2015 using databases, such as PubMed, Medline, Web of Science, and EMBASE was undertaken. RESULTS: In comparison to the old fluorescein dye, ICG has a superior side effect profile and can be accurately detected by various commercial devices, such as SPY Elite (Novadaq, Canada), FLARE (Curadel LLC, USA), PDE-Neo (Hamamatsu Photonics, Japan), Fluobeam 800 (Fluoptics, France), and IC-View (Pulsion Medical Systems AG, Germany). In breast reconstruction, ICG has established as a safer, more accurate tracer agent, in lieu of the traditional blue dyes, for detection of sentinel lymph nodes with radioactive isotopes ((99m)-Technetium). In prosthesis-based breast reconstruction, intraoperative assessment of the mastectomy skin flap to guide excision of hypoperfused areas translates to improved clinical outcomes. Similarly, in autologous breast reconstructions, FA can be utilized to detect poorly perfused areas of the free flap, evaluate microvascular anastomosis for patency, and assess SIEA vascular territory for use as an alternative free flap with minimal donor site morbidity. CONCLUSIONS: ICG-based FA is a novel, useful tool for various applications in breast reconstruction. More studies with higher level of evidence are currently lacking to validate this technology.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it