Use of fluorescence imaging and indocyanine green during colorectal surgery: Results of an intercontinental Delphi survey
Bibliographic record
Abstract
BACKGROUND: Fluorescence imaging with indocyanine green is increasingly being used in colorectal surgery to assess anastomotic perfusion, and to detect sentinel lymph nodes. METHODS: In this 2-round, online, Delphi survey, 35 international experts were asked to vote on 69 statements pertaining to patient preparation and contraindications to fluorescence imaging during colorectal surgery, indications, technical aspects, potential advantages/disadvantages, and effectiveness versus limitations, and training and research. Methodological steps were adopted during survey design to minimize risk of bias. RESULTS: More than 70% consensus was reached on 60 of 69 statements, including moderate-strong consensus regarding fluorescence imaging's value assessing anastomotic perfusion and leak risk, but not on its value mapping sentinel nodes. Similarly, although consensus was reached regarding most technical aspects of its use assessing anastomoses, little consensus was achieved for lymph-node assessments. Evaluating anastomoses, experts agreed that the optimum total indocyanine green dose and timing are 5 to 10 mg and 30 to 60 seconds pre-evaluation, indocyanine green should be dosed milligram/kilogram, lines should be flushed with saline, and indocyanine green can be readministered if bright perfusion is not achieved, although how long surgeons should wait remains unknown. The only consensus achieved for lymph-node assessments was that 2 to 4 injection points are needed. Ninety-six percent and 100% consensus were reached that fluorescence imaging will increase in practice and research over the next decade, respectively. CONCLUSION: Although further research remains necessary, fluorescence imaging appears to have value assessing anastomotic perfusion, but its value for lymph-node mapping remains questionable.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".