Interstitial MR lymphangiography for the detection of sentinel lymph nodes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The challenge for implementation of sentinel lymph node biopsy is to develop a reliable minimally invasive technique that identifies all possible sentinel nodes with high temporal and spatial resolution. This study evaluated the use of a magnetic resonance imaging (MRI) contrast agent (USPIO) for preoperative sentinel node detection. METHODS: Anesthetized pigs received interstitial or intradermal injections of ultra small superparamagnetic of iron oxide (USPIO) (0.2 or 5 mg Fe) in the L/R posterior tongue and stifles (knee) respectively. MRI was done before, during injection and at 0.25, 0.5, 1, 2, 4, 6, 24, and 48 hr after which isosulfan blue sentinel node mapping was done. RESULTS: In the tongue, both doses of USPIO identified the sentinel node in the early images. No additional nodes were detected by MR at 24 or 48 hr. In the hind limb, sentinel nodes identified on the early MR images were also identified by the isosulfan blue. In both locations, the higher dose also identified secondary nodes some of which were also identified by the isosulfan blue. All sentinel nodes that were identified by USPIO on MRI were noted to be stained brown at the time of dissection. CONCLUSIONS: Interstitial MR lymphangiography is a useful technique for the detection of sentinel lymph nodes. This method provides excellent simultaneous temporal and spatial resolution, is minimally invasive, and can be performed preoperatively.
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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 | 0.000 |
| 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 | 0.001 |
| 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".