Validation of EBUS-TBNA-integrated nodal staging in potentially node-positive non-small cell lung cancer
Bibliographic record
Abstract
OBJECTIVE: Nodal staging of lung cancer is important for selecting surgical candidates. Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) was evaluated as a modality for nodal staging of patients with potentially node-positive non-small cell lung cancer (NSCLC). METHODS: Endobronchial ultrasound-guided transbronchial needle aspiration was used for nodal staging of NSCLC patients with radiological N2/3 disease (short axis >10 mm on computed tomography and/or standardized positron emission uptake value >2.5 on 2-deoxy-2[F-18] fluoro-D-glucose positron emission tomography), T-stage ≥ T2, or positive serum carcinoembryonic antigen. Data on eligible patients were extracted from the database of our institution and analyzed for differences in nodal stages between radiological staging (RS) and EBUS-TBNA-integrated staging (ES), with validation by pathological staging of patients who had undergone surgery. RESULTS: Of 480 eligible patients, there were 135 N0/1 and 345 N2/3 patients according to RS. Out of the 345 patients staged as N2/3 by RS, 113 (33 %) were downgraded to N0/1 by ES. Out of the 135 patients staged as N0/1 by RS, 12 (9 %) were upgraded to N2/3 by ES. Patients were restaged as N0/1 in 236 cases and N2/3 in 244 cases by ES, and the distributions of nodal stage between RS and ES were significantly different (p < 0.001). Finally, 215 out of the 236 ES-N0/1 patients underwent lung resection, and 195 (90.7 %) and 20 patients were staged by pathology as N0/1 and N2, respectively. CONCLUSIONS: Endobronchial ultrasound-guided transbronchial needle aspiration is more accurate for lymph node staging compared to radiological staging. EBUS-TBNA can identify patients who are true candidates for surgery.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".