Liquid-Based Cytology in Fine-Needle Aspiration of Breast Lesions: A Review
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: Fine-needle aspiration (FNA) is a safe and cost-effective technique for the diagnosis of breast lesions, especially when correlated with clinical and imaging studies. However, the success of breast FNA is highly dependent on the adequate preparation of cytological conventional smears (CS). The liquid-based cytology (LBC) technique consists of an automated method for preparing thin-layer cytological samples from cell suspensions collected in alcohol-based preservative. LBC is designed to improve CS by avoiding limiting factors such as obscuring material, air-drying and smearing artifacts. STUDY DESIGN: We performed a review of the published literature about LBC applied to breast FNA. RESULTS: LBC preparations of breast aspirates demonstrated better cellular preservation, less cell overlapping and elimination of blood and excessive inflammation compared to CS. Conversely, alterations in architecture and cell morphology as well as loss of myoepithelial cells and stromal elements have been described in LBC specimens, requiring training before applying this technique for diagnosis. Studies have shown a similar accuracy between LBC and CS for the diagnosis of breast lesions. LBC also permits the use of residual material for ancillary tests, which is an important advantage compared to CS. CONCLUSIONS: LBC can be safely applied to breast FNA, showing a similar diagnostic accuracy to CS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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