Sentinel lymph node mapping using ICG fluorescence and cone beam CT – a feasibility study in a rabbit model of oral cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Current sentinel lymph node biopsy (SLNB) techniques, including use of radioisotopes, have disadvantages including the use of a radioactive tracer. Indocyanine green (ICG) based near-infrared (NIR) fluorescence imaging and cone beam CT (CBCT) have advantages for intraoperative use. However, limited literature exists regarding their use in head and neck cancer SLNB. METHODS: This was a prospective, non-randomized study using a rabbit oral cavity VX2 squamous cell carcinoma model (n = 10) which develops lymph node metastasis. Pre-operatively, images were acquired by MicroCT. During surgery, CBCT and NIR fluorescence imaging of ICG was used to map and guide the SLNB resection. RESULTS: Intraoperative use of ICG to guide fluorescence resection resulted in identification of all lymph nodes identified by pre-operative CT. CBCT was useful for near real time intraoperative imaging and 3D reconstruction. CONCLUSIONS: This pre-clinical study further demonstrates the technical feasibility, limitations and advantages of intraoperative NIR-guided ICG imaging for SLN identification as a complementary method during head and neck surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it