Robotic One Anaesthetic Marking And Resection ( <scp>ROMAR</scp> ): A Novel Approach for Thoracoscopic Sublobar Resection of Peripheral Pulmonary Lesions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Small lung nodules can be difficult to identify via thoracoscopy, thereby making thoracoscopic sublobar resection challenging. We have developed a protocol of preoperative lung nodule marking using robotic-assisted bronchoscopy (RAB), followed by thoracoscopic sublobar resection in one setting. We evaluated the outcomes of patients undergoing Robotic One Anaesthetic Marking And Resection (ROMAR). METHODS: We reviewed the records of all patients undergoing ROMAR between January 2023 and March 2025. The primary outcome was the success of marking, defined as dye visualisation on the pleural surface during thoracoscopy and the resected specimen containing the lesion. RESULTS: A total of 119 lesions in 111 patients were included. The mean lesion size was 14.2 mm (SD ± 5.9 mm). The majority of the lesions (77.3%) were non-solid. An average of 1.0 cc of dye was injected per target. RATS and VATS were performed in 77 and 31 patients, respectively. The initial resection procedures were wedge resection in 94 patients (84.7%), segmentectomy in 16 patients (14.4%), and lobectomy in 1 patient (due to adhesions). In 6 cases, dye was not visible on the pleural surface during thoracoscopy. In 5 patients (4.5%), no tumour was identified on the initial frozen section. Therefore, initial sublobar resection was successful in 99 patients (89.2%). No significant complications were noted. CONCLUSIONS: ROMAR facilitated successful thoracoscopic sublobar resection of small lung nodules in approximately 90% of the cases. It is safe and can be easily adopted at most centres performing robotic bronchoscopy and thoracoscopic lung surgery.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".