P.156 The role of indocyanine green fluorescence in the treatment of pituitary tumors
Bibliographic record
Abstract
Background: Biochemical cure in functional pituitary adenomas (FPA) is crucial for reducing patient morbidity and improving quality of life following endoscopic endonasal procedures (EEA). The extent of resection plays a key role in achieving these outcomes. However, even with the aid of intraoperative navigation, complete resection of tumor components can be challenging due to the difficulty in distinguishing them from normal pituitary tissue. Indocyanine green (ICG) fluorescence has been used effectively in various cranial and spinal procedures, but its role in endoscopic skull base surgery has not yet been routinely established Methods: In this study, we describe our experience using ICG during EEA for the resection of FPA. Results: We discuss the fluorescence profiles of both adenomas and normal gland tissue. ICG helped identify additional tumor tissue that was not initially detected after macroscopic adenoma resection. It also allowed for perfusion assessment of the pituitary gland and nasoseptal flaps. No complications were observed following the ICG injection, and biochemical cure was achieved in more than 90% of cases. Conclusions: Our experience suggests that ICG is a safe and promising tool, improving both the extent of resection and endocrinologic outcomes in patients undergoing EEA for FPA.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".