Dosing of indocyanine green for intraoperative laser fluorescence angiography in kidney transplantation
Bibliographic record
Abstract
OBJECTIVE: Sufficient blood supply is a crucial factor determining postoperative allograft function in kidney transplantation. Therefore, besides the surgeon's individual impression, a method for evaluating the quality of the organ's microperfusion is required. Laser fluorescence angiography with indocyanine green (ICG) is an emerging tool for this purpose. However, no reproducible quantification of ICG fluorescence has been performed in transplantation so far. METHODS: system (NOVADAQ, Canada) was employed for quantitative assessment of allograft microperfusion. ICG was administered systemically 5 minutes after reperfusion applying doses between 0.25 and 0.01 mg ICG per kg body weight. Quantitative assessment was performed with the implemented SPY-Q Software. RESULTS: A total of 57 kidney recipients were included in two centers. The generated curves showing ICG IN and EgR were not evaluable due to oversensing when doses exceeded 0.02 mg per kg body weight. CONCLUSIONS: Fluorescence angiography with ICG is an emerging tool for the intraoperative quality control and evaluation of microperfusion in kidney transplantation. A dose of 0.02 mg ICG per kg body weight is recommended to ensure the quantitative assessment with SPY-Q.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".