Robotic segmentectomy for early-stage lung cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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) | 0.003 | 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 it