En Bloc Resection of Pulmonary Sulcus Non-small Cell Lung Cancer Invading the Spine
Bibliographic record
Abstract
OBJECTIVE: To conduct a systematic literature review and pooled data analysis focusing on outcome after en bloc resection of pulmonary sulcus non-small cell lung cancer (NSCLC) invading the spine. BACKGROUND: This rare type of NSCLC has historically been considered unresectable and fatal. Nowadays, carefully selected patients can be cured when treated surgically within a multimodality concept. METHODS: The MEDLINE database was searched using the PubMed engine to retrieve relevant articles. Corresponding authors were contacted, and shared data were pooled and analyzed. RESULTS: Search strategy yielded 134 articles. Six were relevant and nonduplicative. Four authors shared updated data on 135 patients. All tumors were resected en bloc with the lung, chest wall, and spine. Induction was administered in 85 patients (63%) and consisted of chemotherapy (n = 32), radiation (n = 1), or concurrent chemoradiation (n = 52). Spine resections included total (n = 23), hemi- (n = 94), and partial (n = 18) vertebrectomies. R0 resection was achieved in 120 patients (89%). Adjuvant treatment was administered to 70 patients (52%) and included chemotherapy (n = 16), radiotherapy (n = 22), or chemoradiation (n = 32). Overall, 3-, 5-, and 10-year survival rates were 57%, 43%, and 27%, respectively. Univariate analysis identified the type of resection (R0 vs R1/R2, P < 0.001) as significant prognostic factor among the variables tested (age, histology, pT/pN, type of induction/adjuvant treatment, type of lung/spine resection). CONCLUSIONS: Multimodality therapy including en bloc resection for pulmonary sulcus NSCLC invading the spine provides excellent long-term survival in selected patients. This result establishes a benchmark against which the effects of new treatments can be compared in the future.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".