Comparison of endoscopic ultrasonography‐guided fine‐needle aspiration cytology results with and without the stylet in 3364 cases
Bibliographic record
Abstract
BACKGROUND AND AIM: Endoscopic ultrasound-guided fine-needle aspiration cytology (EUS-FNA) is traditionally carried out with the stylet, as it is believed to prevent blockage or contamination of the needle by tissue coming from the gastrointestinal wall. However, this recommendation has not been demonstrated on an empirical basis. The aim of the present study was to compare the yield of EUS-FNA in a very large series of patients with (S+) and without (S-) the stylet. METHODS: Until 2004, the stylet was used for EUS-FNA in our center. After that, the stylet was never used. The results of all EUS-FNA in solid lesions carried out by one endosonographer with the same needle type were compared before and after stylet use was stopped. RESULTS: 3364 EUS-FNA procedures (in 3078 patients) in solid lesions were included (1483 S+ and 1881 S-). There was no significant difference between the S+ and S- results for any variable other than the number of passes required. The number of passes was significantly lower in the S- group when sampling lymph nodes, wall lesions and when carrying out biopsies through the gastric or rectal wall. However the statistical differences disappeared after controlling for malignancy, location and lesion size. CONCLUSION: This very large comparative study showed no benefit in diagnostic yield when using the stylet for EUS-FNA.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".