Comparing the safety and effectiveness of surgical approaches in thymectomy
Bibliographic record
Abstract
Abstract Objective To compare the safety and effectiveness of different surgical approaches in thymectomy: robotics, subxiphoid, lateral video‐assisted thoracoscopy surgery (LVATS) and open. Methodology We retrospectively reviewed 68 cases of thymectomy with a robot‐assisted, subxiphoid, LVATS, open sternotomy or thoracotomy approach for thymic lesions or myasthenia gravis between July 2017 and May 2023 at a single centre. Peri‐operative outcomes (operating time, estimated blood loss, conversion rates, R0 resection, adverse events and length of stay [LOS]) were collected. Results We observed six conversions to open (from five LVATS and one robot assisted). The median estimated blood loss was lower for LVATS (100.00 [50.0–100.0] mL) compared with open thymectomies (200.0 [150.0–400.0]; P < .001). No intra‐operative adverse events were reported in the robotics, subxiphoid or LVATS groups. In patients with thymic tumours ( n = 34), R0 resection was achieved in 100% (2/2) of robotics, 83% of subxiphoid (5/6), 93% (13/14) of LVATS and 75% ( n = 9/12) of open cases. The median LOS was shortest for robot assisted (1.0 [interquartile range (IQR) 1.0–3.0]), then subxiphoid (2.0 [IQR 1.0–3.0]), LVATS (2.0 [IQR 1.0–3.0]) then open (5.0 [IQR 4.0–6.0]; P < .001). Conclusions Our results suggest that with a shorter LOS, robotics, subxiphoid and LVATS thymectomies are safe. Larger size studies are required to compare R0 resection rates between these less invasive surgical approaches.
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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 | 0.003 | 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".