Robotic segmentectomy for early-stage lung cancer
Bibliographic record
Abstract
Lobectomies have long been the gold standard for surgical treatment of early-stage non-small cell lung cancer (NSCLC), with segmentectomies limited to instances of benign disease or as an alternative in patients where lung preservation is indicated. However, a recently published randomized control trial has demonstrated the superiority of segmentectomy over lobectomy in terms of overall survival for early-stage lung cancer. Segmentectomy could thus be considered a standard procedure for small-sized peripheral NSCLC. While segmentectomy via video-assisted thoracic surgery (VATS) is the most widespread approach, development in video instrumentation and thoracic robotic surgery is rapidly gaining interest. Indeed, robotic surgery pioneers boast the advantages in three-dimensional view, improved magnification, ergonomics, dexterity, safety, and ease of surgery with this technology. This review aims to outline robotic-assisted segmentectomy indications, preoperative evaluation, and the operative conduct for the different lung segments from a single surgeon console. There are many ways to perform segmentectomies and therefore this review describes generalized approaches that can be tailored based on experience.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".