The Milan System for Reporting Salivary Gland Cytopathology (MSRSGC): An ASC-IAC-Sponsored System for Reporting Salivary Gland Fine-Needle Aspiration
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
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.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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