Mapping sentinel lymph nodes in cutaneous melanoma: a vast array of perioperative imaging modalities
Bibliographic record
Abstract
Sentinel lymph node biopsy (SLNB) is a decisive step in the staging process of melanoma, critically impacting patients' oncological outcome and driving the decision-making process. SLNB limits the extent of the dissection in cases where no metastases are found. Conversely, when metastases are detected, SLNB has the potential to improve regional control of the disease when complete lymphadenectomy or early administration of adjuvant treatment are indicated. Thus, accurately identifying sentinel lymph nodes represents an important prognostic factor. Several strategies have been studied, including novel procedures that are not commonly used in the clinical setting. This review highlights the different tracers, preoperative and intraoperative imaging modalities studied to perform SLNB in cutaneous melanoma. The development of innovative modalities has been fueled by a need to optimize current approaches, offering new alternatives that can overcome some of the limitations of the standard method.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".