The Milan System for Reporting Salivary Gland Cytopathology (MSRSGC): An ASC-IAC-Sponsored System for Reporting Salivary Gland Fine-Needle Aspiration
Bibliographic record
Abstract
The diagnostic role of salivary gland fine-needle aspiration (SG-FNA) is well established in the preoperative evaluation of patients with salivary gland lesions. At present, most salivary SG-FNA specimens are diagnosed based on conventional diagnostic criteria. However, there exists a lack of uniform reporting for these specimens to guide the clinical management of patients. This void motivated a group of experienced cytopathologists to spearhead the development of a uniform reporting system. This international panel, under the sponsorship of the American Society of Cytopathology (ASC) and the International Academy of Cytology (IAC), gathered in September 2015 at the European Congress of Cytology, held in Milan, Italy, to propose the “Milan System for Reporting Salivary Gland Cytopathology” (MSRSGC). This effort sparked the interest of many and brought forth an agreement to develop an evidence-based tiered classification consisting of 6 diagnostic categories. It is hoped that this standard reporting system will enhance the overall effectiveness of SG-FNA reporting across institutions, with the ultimate result being better communication and improved patient care.
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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 | 0.067 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".