Comparison of Diagnostic Yield and Performance of Franseen Tip Needle Versus Non-Franseen Tip Needle in Patients Undergoing Endobronchial Ultrasound‑Guided Transbronchial Needle Sampling for Undiagnosed Mediastinal Lymphadenopathy
Bibliographic record
Abstract
BACKGROUND: Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is now the standard for diagnosing mediastinal lymphadenopathy. Franseen tip fine-needle biopsy (FNB) needles have been proposed to improve diagnostic yield, but comparative evidence remains limited. METHODS: A systematic literature search was conducted in PubMed, Scopus, and Google Scholar until October 10, 2024. Studies were included based on the PICO framework, focusing on adults undergoing EBUS transbronchial needle sampling for mediastinal lymphadenopathy. Data extraction included study characteristics, participant demographics, and diagnostic yield outcomes. Study quality was assessed using the Newcastle-Ottawa Scale. The pooled odds ratio (OR) with 95% CI was calculated using MetaXL software, and heterogeneity was assessed with the I² statistic. The review protocol was registered with PROSPERO (CRD42024559634). RESULTS: Of 3343 screened articles, 6 studies involving 646 patients met the inclusion criteria. The pooled diagnostic yield was 82.5% (346 out of 405) with the Franseen needle versus 75.9% (245 out of 323) with the non-Franseen needle. The pooled OR for diagnostic yield (DY) with FNB needle was 1.64 (95% CI: 1.11-2.43), indicating a statistically significant advantage over non-Franseen needles. The increase in DY with FNB needle is seen in benign diseases (83.3% vs. 71.1%), but not in malignant diseases (92.5% vs. 80.4%). Core tissue acquisition rate, and sample adequacy for molecular analysis and PD-L1 testing were similar between the Franseen needle and conventional TBNA needles. No significant complications were reported with the use of FNB needles. CONCLUSION: Franseen tipped FNB needles offer a superior diagnostic yield compared with non-Franseen needles in benign diseases. Larger randomized trials are needed to reconfirm these findings.
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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 | 0.023 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.019 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".