Fine‐needle aspiration of cutaneous, subcutaneous, and intracavitary masses in dogs and cats using 22‐ vs 25‐gauge needles
Bibliographic record
Abstract
BACKGROUND: Fine-needle aspiration (FNA) is a common procedure as a diagnostic tool in veterinary medicine. However, it is unclear whether the gauge of the needle affects the quality of cytology. OBJECTIVE: This study compared the quality of cytologic samples obtained via FNA using 22- or 25-gauge needles. METHODS: Fine-needle aspiration was performed on 50 masses (cutaneous, subcutaneous, or intracavitary) obtained from client-owned animals. The size of the needle was randomly assigned using either of the following two sequences: 22-25-22 gauge or 25-22-25 gauge. Samples were evaluated by two board-certified clinical pathologists to assess cellularity, blood contamination, amount of cellular debris, degree of cellular trauma, and the overall ability to make a diagnosis for each sample. RESULTS: No significant difference was detected between the 22- and 25-gauge needle samples for cellularity, whereas a significant difference was present for blood contamination, amount of cellular debris, and degree of cellular trauma. The overall ability to make a diagnosis was not significantly affected by the needle gauge. The degree of cellular trauma was significantly increased in intracavitary samples. CONCLUSIONS AND CLINICAL RELEVANCE: Needle gauge is a contributing factor to FNA sample quality. However, it did not affect the overall ability to make a diagnosis. Samples obtained using 25-gauge needles resulted in less blood contamination yet increased cellular trauma compared to 22-gauge needle samples.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".