Utility of Multistep Protocols in the Analysis of Sentinel Lymph Nodes in Cutaneous Melanoma: An Assessment of 194 Cases
Bibliographic record
Abstract
CONTEXT.—: Currently, no universal protocol exists for the assessment of sentinel lymph nodes (SLNs) in cutaneous melanoma. Many institutions use a multistep approach with multiple hematoxylin-eosin (H&E) and immunohistochemical stains. However, this can be a costly and time- and resource-consuming task. OBJECTIVE.—: To assess the utility for multistep protocols in the analysis of melanoma SLNs by specifically evaluating the Calgary Laboratory Services (CLS) protocol (which consists of 3 H&E slides and 1 S100 protein, 1 HMB-45, and 1 Melan-A slide per melanoma SLN block) and to develop a more streamlined protocol. DESIGN.—: Histologic slides from SLN resections from 194 patients with diagnosed cutaneous melanoma were submitted to the CLS dermatopathology group. Tissue blocks were processed according to the CLS SLN protocol. The slides were re-reviewed to determine whether or not metastatic melanoma was identified microscopically at each step of the protocol. Using SPSS software, a decision tree was then created to determine which step most accurately reflected the true diagnosis. RESULTS.—: We found with Melan-A immunostain that 337 of 337 negative SLNs (100%) were correctly diagnosed as negative and 55 of 56 positive nodes (98.2%) were correctly diagnosed as positive. With the addition of an H&E level, 393 of 393 SLNs (100%) were accurately diagnosed. CONCLUSIONS.—: We recommend routine melanoma SLN evaluation protocols be limited to 2 slides: 1 H&E stain and 1 Melan-A stain. This protocol is both time- and cost-efficient and yields high diagnostic accuracy.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".