The Milan System for Reporting Salivary Gland Cytopathology: Benefits and Cautions
Bibliographic record
Abstract
Abstract Fine-needle aspiration (FNA) is a well-established procedure for the diagnosis and management of salivary gland lesions despite challenges imposed by their diversity, complexity, and cytomorphological overlap. Until recently, the reporting of salivary gland FNA specimens was inconsistent among different institutions throughout the world, leading to diagnostic confusion among pathologists and clinicians. In 2015, an international group of pathologists initiated the development of an evidence-based tiered classification system for reporting salivary gland FNA specimens, the Milan System for Reporting Salivary Gland Cytopathology (MSRSGC). A corresponding MSRSGC Atlas was published in February 2018. The MSRSGC consists of 6 diagnostic categories that incorporate the morphologic heterogeneity and overlap among various nonneoplastic, benign, and malignant lesions of the salivary glands. In addition, each diagnostic category is associated with a risk of malignancy and management recommendations. The main goal of the MSRSGC is to improve communication between cytopathologists and treating clinicians, while also facilitating cytologic-histologic correlation, quality improvement, and sharing of data from different laboratories for research. Herein, we review the benefits and the limitations of the MSRSGC, as well as the challenges of implementing this new reporting system in routine practice.
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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 | 0.061 | 0.140 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".