Identification of lymphatic pathway involved in the spread of bladder cancer: Evidence obtained from fluorescence navigation with intraoperatively injected indocyanine green
Bibliographic record
Abstract
INTRODUCTION: We identify lymphatic vessels draining from the bladder by using fluorescence navigation (FN) system. METHODS: In total, 12 candidates for radical cystectomy and pelvic lymph node dissection (PLND) were included in this study. After an indocyanine green (ICG) solution was injected into the bladder during radical cystectomy, lymphatic vessels draining from the bladder were analyzed using a FN system. PLND was based on the lymphatic mapping created from the FN measurements (in vivo probing) in the external iliac, obturator and internal iliac regions; after PLND, the fluorescence of the removed lymph nodes (LNs) was analyzed on the bench (ex vivo probing). RESULTS: There were no patients with complications associated with the intravesical ICG injection. A lymphatic pathway along inferior vesical vessels to internal iliac LNs was clearly illustrated in 7 cases. Under in-vivo probing, the fluorescence intensity of internal iliac nodes was greater than that of external iliac or obturator nodes. Under ex-vivo probing, the fluorescence intensity of internal iliac and obturator nodes was greater than that of external iliac nodes. CONCLUSIONS: Using an FN system after injecting ICG during a radical cystectomy operation is a safe and rational approach to detecting the lymphatic channel draining from the bladder.
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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 | 0.000 | 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 | 0.000 |
| 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".