Prognostic Significance of the Number of Removed Lymph Nodes at Lobectomy in Patients with Positron Emission Tomography-Computed Tomography-Negative N2 Non-Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: We assessed the relation between the extent of lymph node (LN) dissection and the prognosis for positron emission tomography-computed tomography (PET-CT)-negative patients with clinical early-stage non-small cell lung cancer (NSCLC), undergoing lobectomy and mediastinal LN dissection. METHODS: 277 patients with clinical stage I/II NSCLC who had undergone a preoperative PET-CT scan followed by lobectomy were analysed retrospectively. The prognostic value of age, maximum standardized uptake value (SUVmax) of the tumour, tumour size, carcinoembryonic antigen and number of dissected LNs was assessed to determine any association with overall survival and disease-free survival. RESULTS: 31 patients developed postoperative relapse, and multiple logistic regression revealed that the number of dissected LNs was an independent factor predicting relapse. Patients were categorized into groups according to the number of LNs dissected (group I, < 10; group II, ≥ 10). There were no statistical differences between 2 groups but group II patients had a lower relapse rate (6.3%, p = 0.003) and better disease-free survival (74.95 months, p = 0.045). CONCLUSIONS: Mediastinal LN dissection is still important for clinical early-stage NSCLC patients undergoing lobectomy even when the preoperative PET-CT is negative, and results in fewer relapses and improved disease-free survival.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".