Diagnosis and subclassification of lymphomas and non‐neoplastic lesions involving mediastinal lymph nodes using endobronchial ultrasound‐guided transbronchial needle aspiration
Bibliographic record
Abstract
INTRODUCTION: The value of endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) has been established for staging mediastinal lymph nodes in lung carcinoma patients with radiologically enlarged lymph nodes, but its utility for evaluation of primary lymph node disorders is not well defined. The objective of this study was to evaluate the usefulness of EBUS-TBNA with on-site assessment and triage of sample for multiple ancillary techniques, for the diagnosis and subclassification of lymphomas and non-neoplastic lesions involving mediastinal lymph nodes. METHODS: One hundred and twenty consecutive patients who underwent EBUS-TBNA between January 2008 and August 2009 were reviewed. The final cytological diagnosis was based on air-dried Romanowsky and alcohol-fixed Papanicolaou stained direct smears, immunohistochemistry, immunophenotyping, and fluorescence in situ hybridization (FISH). RESULTS: A total of 38 cases were included in this study consisting of eight reactive lymphoid hyperplasia, 20 granulomatous lymphadenitis (17 non-necrotizing and 3 necrotizing granulomatous inflammations), 3 Hodgkin lymphomas and 7 non-Hodgkin lymphomas (1 small lymphocytic lymphoma (SLL), 1 SLL with scattered Reed-Sternberg cells, 1 marginal zone lymphoma, and 4 large B cell lymphomas). Cultures performed in 13 cases were negative for AFB and fungi. Immunophenotyping and immunohistochemistry for MIB1 in six cases, and FISH in five cases provided necessary information for subclassification. CONCLUSIONS: EBUS-TBNA is a minimally invasive procedure which provides sufficient sample for definitive primary diagnosis and classification of malignant lymphoma and granulomatous inflammation in patients with mediastinal lymphadenopathy. Rapid on-site specimen assessment is invaluable for appropriate assignment of sample to ancillary studies.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".