Use of Indocyanine Green Fluorescence Angiography to Assess Bowel Anastomosis in Ovarian Cancer Surgery
Bibliographic record
Abstract
BACKGROUND/AIM: The aim of this study was to investigate the efficacy of indocyanine green (ICG) fluorescence angiography in preventing anastomotic leaks and reducing the need for ostomies during cytoreductive surgery in ovarian cancer. PATIENTS AND METHODS: This was a retrospective study of patients with 2014 International Federation of Obstetrics and Gynecology stage IIB-IVB ovarian cancer requiring a bowel resection during primary or secondary cytoreductive surgery at our institution between July 2021 to April 2023. Rates of ostomy performance and anastomotic leak were assessed in the ICG angiography group and the non-ICG angiography group. Frequency distributions between categorical variables were compared using Fisher's exact or Chi-squared test. Wilcoxon rank-sum test and t-test were used to compare continuous variables. RESULTS: During the study period, we reviewed the data of 59 consecutive patients with ovarian cancer with bowel resection; in 30 (50.85%) patients, bowel anastomosis was assessed using ICG angiography and in 29 (49.15%) patients, bowel anastomosis was not assessed using ICG angiography. Anastomotic leak rate was found to be 6.9% (n=2) in the non-ICG angiography group, and 3.33% in the ICG angiography group (n=1) (p=0.612). More diverting ostomies were performed in the non-ICG angiography group (n=6, 20.69%) compared to the ICG angiography group in which no ostomies were performed (p=0.011). CONCLUSION: ICG angiography is not associated with a decrease in anastomotic leak rates, but it may avoid ostomy formation.
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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 | 0.003 |
| 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 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".