Does robotic-assisted thymectomy have advantages over video-assisted thymectomy in short-term outcomes? A systematic view and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: A thymic epithelial tumour is the most common primary tumour in the anterior mediastinum of adults. A few retrospective studies compared the short-term outcomes between robotic-assisted thymectomy (RAT) and video-assisted thymectomy (VAT). So, it is necessary to conduct a meta-analysis to further compare these 2 surgical techniques. METHODS: EMBASE, Medline and Web of Science were used. Thesaurus terms and medical subject headings were used in Medline and EMBASE, respectively. The Newcastle-Ottawa scale was used for grading because the included studies were all case-control studies. RESULTS: Nine studies were included in the meta-analysis with a total of 723 patients, including 315 patients in the RAT group and 408 patients in the VAT group. The meta-analysis [odds ratio (OR) 0.24, 95% confidence interval (CI) 0.06-0.94; P = 0.041], indicating that RAT yielded a significantly lower rate of conversion compared with VAT. Duration of drainage with RAT was significantly less than that with VAT (weighted mean difference = -1.10; 95% CI -1.98 to -0.22; P = 0.014). The pooled analysis (weighted mean difference = -103.6; 95% CI -199.21 to -7.98; P = 0.034) suggested that patients in the RAT group had less drainage than those in the VAT group. The recurrence rates in both groups were comparable (OR 0.19, 95% CI 0.03-1.20; P = 0.078). CONCLUSIONS: RAT has advantages over VAT in terms of short-term outcomes such as shorter duration of drainage, less total drainage and a lower rate of conversion. The recurrence rate was comparable between the 2 techniques. Therefore, RAT could be considered as an alternative treatment for diseases of the thymus.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.053 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".