Mapping sentinel lymph nodes in cutaneous melanoma: a vast array of perioperative imaging modalities
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
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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