Suggested robotic-assisted thoracic surgery training curriculum
Bibliographic record
Abstract
Robotic-assisted surgery, a technological advancement in the field of surgery, has become increasingly popular among surgeons of many specialties over time. Robotic-assisted thoracic surgery (RATS) is comparable to video-assisted thoracic surgery (VATS) in terms of patient care outcomes; however, the perception of increased operative time and a lack of cost-effectiveness have led to controversy regarding its alleged benefits. Nevertheless, robotic surgery is one of the preferred options for minimally invasive surgery by some thoracic surgeon over VATS, due to its ability to provide 3-D vision, precise wrist movements, enhanced magnification, and instrument stability and articulation. Notably, trainees in the field of thoracic surgery experience difficulty gaining knowledge and learning skills associated with RATS due to its complexity, limited access to robotic instruments, the lack of a standardized curriculum for trainees, and lack of mentorship or proctorship, thus leading to a steeper learning curve compared to laparoscopic or VATS procedures that are cost-friendly, easy to learn, and feasible to practice. Nevertheless, focusing on RATS training for thoracic surgeons will keep them familiar with robotic techniques, including the pre-operative setup and intra-operative process, which will ultimately decrease operative times. In this paper, we will review the literature, express and discuss the most viable training curriculum from authors' point of view to help achieve this goal.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".