A Proposal for the Performance, Classification, and Reporting of Lymph Node Fine-Needle Aspiration Cytopathology: The Sydney System
Bibliographic record
Abstract
BACKGROUND: The evaluation of lymph nodes (LN) by fine-needle aspiration cytology (FNAC) is routinely used in many institutions but it is not uniformly accepted mainly because of the lack of guidelines and a cytopathological diagnostic classification. A committee of cytopathologists has developed a system of performance, classification, and reporting for LN-FNAC. METHODS: The committee members prepared a document that has circulated among them five times; the final text has been approved by all the participants. It is based on a review of the international literature and on the expertise of the members. The system integrates clinical and imaging data with cytopathological features and ancillary techniques. The project has received the endorsement and patronage of the International Academy of Cytology and the European Federation of the Cytology Societies. RESULTS: Clinical, imaging, and serological data of lymphadenopathies, indications for LN-FNAC, technical procedures, and ancillary techniques are evaluated with specific recommendations. The reporting system includes two diagnostic levels. The first should provide basic diagnostic information and includes five categories: inadequate/insufficient, benign, atypical lymphoid cells of undetermined/uncertain significance, suspicious, and malignant. For each category, specific recommendations are provided. The second diagnostic level, when achievable, should produce the identification of specific benign or malignant entities and additional information by utilizing ancillary testing. CONCLUSION: The authors believe that the introduction of this system for performing and reporting LN-FNAC may improve the quality of the procedure, the report, and the communication between cytopathologists and the clinicians. This system may lead to a greater acceptance and utilization of LN-FNAC and to a better interdisciplinary understanding of the results of this procedure.
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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 | 0.120 | 0.110 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.017 | 0.009 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".