Role of intrapulmonary lymph nodes in patients with NSCLC and visceral pleural invasion. The VPI 1314 multicenter registry study protocol
Bibliographic record
Abstract
BACKGROUND: In the lung cancer classification (TNM), the involvement of thoracic lymph nodes is relevant from a diagnostic and prognostic point of view. Even if imaging modality could help in selecting patients who should undergo surgery, a systematic lymph node dissection during lung surgery is mandatory to identify the subgroup of patients who can benefit from an adjuvant treatment. METHODS: Patients undergoing elective lobectomy/bilobectomy/segmentectomy) for non-small cell lung cancer and lymphadenectomy with lymph nodes station 10-11-12-13-14 sampling that meet the inclusion and exclusion criteria will be recorded in a multicenter prospective database. The overall incidence of N1 patients (subclassified in: Hilar Lymph nodes, Lobar Lymph nodes and Sublobar Lymph nodes) will be examined as well as the incidence of visceral pleural invasion. DISCUSSION: The aim of this multicenter prospective study is to evaluate the incidence of intrapulmonary lymph nodes metastases and the possible relation with visceral pleural invasion. Identifying patients with lymph node station 13 and 14 metastases and/or a link between visceral pleural invasion and presence of micro/macro metastases in intrapulmonary lymph nodes may have an impact on decision-making process. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT05596578.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".