Effect of volume and methylene blue on fluorescence intensity and transit of indocyanine green for sentinel lymph node mapping in a simulated feline tumor model
Bibliographic record
Abstract
OBJECTIVE: To compare the effect of volume and solution on transit time and fluorescence intensity (FI) using near-infrared fluorescence imaging (NIRF) in a simulated tumor model in cats. Secondarily, to describe SLN mapping with indocyanine green (ICG) NIRF and report any adverse effects of intradermally injected ICG in cats. ANIMALS: 7 healthy purpose-bred domestic shorthaired male cats. METHODS: Cats were randomly divided into 2 groups (ICG or ICG + methylene blue [MB]). Transit time and FI were determined for 1 or 2 mL solutions injected intradermally in 4 quadrants around a simulated tumor. Following massage, fluorescence intensity was quantified by calculating the corrected total ROI fluorescence using ImageJ software. Cats were monitored for adverse effects up to 4 weeks post-injection. RESULTS: A larger solution volume had decreased transit times to the SLN (P = .001). There was no significant difference in transit times between ICG and ICG-MB. ICG demonstrated a greater FI (P = .001) in the SLN compared to ICG-MB. Methylene blue did not percutaneously fluoresce under NIRF. The volume of the solution did not significantly affect FI. No adverse reactions were reported. CLINICAL RELEVANCE: Increased volume of ICG may aid in rapid percutaneous lymphatic tracking from tumor to SLN. Indocyanine green alone may be preferred over ICG-MB for greater visualization of the SLN. Intradermal injections of ICG and ICG-MB were well-tolerated in healthy cats with no significant complications. Clinical evaluation of this technique in an impaired lymphatic system, as seen in cancer patients, should be assessed in future research.
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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 | 0.000 |
| 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".