Molecular Diagnosis Using Residual Liquid-Based Cytology Materials for Patients with Nondiagnostic or Indeterminate Thyroid Nodules
Bibliographic record
Abstract
The Bethesda System for reporting thyroid fine-needle aspiration (FNA) specimens1 undoubtedly represents a major step toward standardization, reproducibility, and ultimately improved clinical significance, usefulness, and predictive value of thyroid FNA. During the past decade, several classification schemes for thyroid gland FNA have been proposed by various professional organizations.1–6 Most of these schemes consist of 4 to 6 diagnostic categories, which are not always comparable with each other. This variation has led to confusion and differences in perceptions of diagnostic terminology in cytopathology reporting of thyroid FNA between cytopathologists and clinicians.7,8 The main difficulty is represented by “borderline” lesions characterized by atypia of undetermined significance and/or a microfollicular pattern.9,10 In this context, it is interesting to compare the division in 6 classes proposed by the National Cancer Institute (NCI)1,2 with the division in 5 classes proposed by the British Association–Royal College of Physicians in 20024,5 and modified by the Italian Society of Pathology and Cytopathology–Italian Section of the International Academy of Pathology in 2007.6 All classification systems provide a category for nondiagnostic FNA samples, a category for benign lesions, and a category for malignant lesions. However, there are also notable differences. The NCI system, as illustrated in Table 1, introduces 2 categories for borderline lesions: “atypia/follicular lesion of undetermined significance” and “follicular neoplasm or suspicious for a follicular neoplasm.” Conversely, the British and the Italian reporting systems provide a single category for all borderline lesions, named follicular lesion and follicular proliferation, respectively. In addition, the British and the Italian systems provide numeric coding for each category.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".