The role of robotic segmentectomy for non-small cell lung cancer
Bibliographic record
Abstract
Segmentectomy may be applied to all segments; superior segmentectomies (lower lobe superior segments for both lungs), lingulectomies (two segments forming lingulas of upper left lobe) and basal segmentectomies (segments other than superior segment for both lungs). In lung segment resections; segmentectomy has an equivalent morbidity, recurrence and survival rate compared to lobectomy, in patients with stage I lung cancer, tumors smaller than 2 cm and within the segmental anatomical limits. Segmentectomy also contributes more to preserving lung function and exercise capacity than lobectomy. In tumor resection; especially in patients with advanced age, insufficient performance or insufficient cardiopulmonary reserve, 2 cm in diameter and acceptable segmental margins may be provided. Limited long-term results show oncological results of robotic approach similar to open and VATS approaches. Robotic approach facilitates surgery with more intuitive movements, greater flexibility and high definition, three-dimensional vision. However, high cost and lack of touch sense are main disadvantages of robotic surgery. New studies are needed to assess quality of life, morbidity, oncological results and cost effectiveness. However, considering development of technology in our age and fact that many surgical robot brands will be released in the near future, it is predicted that disadvantages of robotic surgery will be minimized in the near future. This article reviews experience of segmentectomy in non-small cell lung cancer and discusses benefits and limitations of robotic segmentectomy.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".