Advances in lung cancer surgery: the role of segmentectomy in early–stage management
Bibliographic record
Abstract
INTRODUCTION: The evolving landscape of surgical interventions for early-stage non-small cell lung cancer (NSCLC) necessitates a reassessment of the traditional gold standard of lobectomy versus emerging sublobar resections, prompting this critical narrative review. AREAS COVERED: This review encompasses recent randomized controlled trials, notably JCOG0802/WJOG4607L and CALGB140503, comparing lobectomy and sublobar resections for early-stage NSCLC, focusing on tumor size and recurrence rates. It also discusses the importance of individualized decision-making, future research avenues, and technological advancements in lung cancer surgery. EXPERT OPINION: In this rapidly evolving field, sublobar resections emerge as a viable alternative to lobectomy for tumors smaller than 2 cm in early-stage NSCLC, necessitating precise patient selection and ongoing technological advancements to optimize outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 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".