Dynamic MR lymphangiography and carbon dye for sentinel lymph node detection: A solution for sentinel lymph node biopsy in mucosal head and neck cancer
Bibliographic record
Abstract
BACKGROUND: The practical application of sentinel lymph node biopsy in squamous cell carcinoma of the head and neck is restricted by the time sensitivity of blue dye and lack of spatial resolution and nonspecific node enhancement with radiocolloid. This study evaluates the use of magnetic resonance (MR) lymphangiography and carbon dye labeling to circumvent these limitations. METHODS: Gadomer/carbon dye mixture was injected into the tongue and stifle of adult swine (n = 4). MR lymphatic mapping was followed by intraoperative mapping with isosulfan blue dye. Sentinel lymph node biopsy and completion node dissection were performed 60 minutes after injection in four nodal basins and at 7 days after injection in eight. RESULTS: The technique was successful in all 12 nodal basins. The sentinel lymph nodes were stained black at the time of the immediate and delayed dissections. CONCLUSIONS: MR lymphangiography provides temporal and anatomic localization of the sentinel lymph node with a single investigation. Carbon dye is a sensitive and persistent visual marker of MRI-targeted sentinel lymph nodes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".