Fluorescence-guided pancreatic surgery: A scoping review
Bibliographic record
Abstract
BACKGROUND: Although fluorescence guidance during various surgical procedures has been shown to be safe and have possible better clinical outcomes than without the guidance, the use of fluorophores in pancreatic surgery is novel and not yet well described. This scoping review involved a systematic methodology of the currently available literature and aimed to illuminate the use of fluorophores in pancreatic surgery from a clinical view. METHODS: The PRISMA and the PRISMA-ScR guidelines were used when appropriate and the following databases were searched: PubMed, Embase, Scopus, The Cochrane Collection, and Web of Science. Human original articles and case reports were included. Bias was assessed with the Newcastle-Ottawa Scale and the IDEAL framework was used for evaluation of surgical innovation. RESULTS: A total of 5,565 search hits were screened, and 23 original articles and 24 case reports consisting of 754 patients met the inclusion criteria. The use of indocyanine green was both the most prominent and the most promising method for securing sufficient perfusion of neighboring organs, enhancing the detection and distinguishing of neuroendocrine tumors, and assisting in the identification of hepatic micrometastases. CONCLUSION: The included studies were generally heterogenic, exploratory, and small. Indocyanine green was used in several ways, and it may add clinical value in different settings during pancreatic surgery. Tumor-targeted probes are a rapidly developing and promising field of research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".