Robotic vs. uniportal lobectomy: a prospective analysis of postoperative pain, analgesic requirements, and individual risk factors
Bibliographic record
Abstract
INTRODUCTION: Lung carcinoma represents a malignancy with the highest global mortality rate. Surgical treatment remains the cornerstone of curative therapy, with minimally invasive techniques currently dominating the field. This study aimed to compare post-operative pain in patients undergoing lobectomy for lung cancer via uniportal video-assisted thoracoscopic surgery (UVATS) vs. robotic-assisted thoracic surgery (RATS). METHODS: This prospective study included 140 patients (70 RATS, 70 UVATS) undergoing lobectomy with mediastinal lymphadenectomy. Patients assessed their pain using the Short-Form McGill Pain Questionnaire and visual analogue scale (VAS) on the 3rd and 14th postoperative days. We also analyzed the influence of age, gender, and BMI on pain perception and analgesic requirements. RESULTS: Patients following RATS exhibited significantly higher pain intensity compared to UVATS on both the 3rd (VAS 5.8 ± 2.0 vs. 3.8 ± 1.6; P < 0.00001) and 14th postoperative days (VAS 2.7 ± 1.1 vs. 2.2 ± 1.1; P = 0.00133). Combined analgesic therapy was more frequently required in the RATS group. Female patients demonstrated markedly higher pain intensity and analgesic requirements in both surgical approaches. Age and BMI had no significant impact on pain perception. CONCLUSION: Robotic-assisted surgery is associated with higher postoperative pain compared to uniportal video-assisted thoracoscopy, with differences being more pronounced in female patients. We recommend implementing targeted analgesic strategies for robotic procedures and considering the use of 8-mm ports instead of standard 12-mm ports to reduce chest wall trauma.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".