Determining agreement between preoperative computed tomography lymphography and indocyanine green near infrared fluorescence intraoperative imaging for sentinel lymph node mapping in dogs with oral tumours
Bibliographic record
Abstract
Lymphatic drainage from the head and neck is variable with significant crossover, therefore sentinel lymph node (SLN) mapping can help ensure the appropriate lymph node(s) are sampled. To improve sensitivity, SLN mapping utilizing multiple modalities and a combination of preoperative computed tomography lymphography (CTL) and intraoperative near infrared fluorescence imaging (NIRF) with indocyanine green (ICG) +/- methylene blue (MB) dye has been suggested. The aim of this study was to describe a method for intraoperative ICG lymphography and determine agreement for SLN detection using preoperative CTL and intraoperative ICG NIRF + MB lymphography (IOL) in dogs with oral tumours. Fourteen client-owned dogs were included. All dogs had preoperative CTL with iodinated contrast and intraoperative IOL with an exoscope. Lymph nodes with CTL contrast-enhancement, blue staining or fluorescence were considered sentinel. The overall SLN identification rate was 100% when CTL and IOL were combined. A total of 57 SLNs were identified. Indocyanine green NIRF identified a greater proportion of SLNs (91%; 52/57) compared with MB (50.8%; 29/57) and CTL (42.1%; 24/57). Eighteen SLNs were identified by all three modalities with a fair level of agreement using Fleiss kappa. These findings suggest a combination of preoperative CTL with intraoperative SLN mapping techniques may greatly improve the ability to accurately detect the SLN in dogs with oral tumours.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".