Single Versus Multi-Incisional Video-Assisted Thoracic Surgery: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Video-Assisted Thoracic Surgery (VATS) is conventionally performed through multiple small incisions (C-VATS). Recent studies have reported encouraging results with the single-incision VATS (S-VATS) over the conventional technique. However, these studies were either small in size, unfocused, nonuniform, retrospective, lacking follow-up information, or focused on pain. We aim to validate previously reported results in a single large meta-analysis, including only the best evidence studies available. METHODS: Systematic review of the PubMed archive was conducted to include only full English articles with Newcastle Ottawa Scale score ≥7. The primary outcome was the complications rate while secondary outcomes were operative time, resected lymph nodes (LNs), chest tube duration, estimated blood loss, length of postoperative stay (LOS), and postoperative pain on day 1 after surgery. Odds ratio and standard mean difference were used as effect estimates. Random model and leave-one-out analysis were used. RESULTS: A total of 39 studies were included with 4635 patients (1686 S-VATS versus 2949 C-VATS). S-VATS has resulted in significantly less postoperative pain (P < .001), blood loss (P = .006), LOS (P < .001), and chest tube duration (P < .001). In lung cancer patients, the number of retrieved LNs was similar to that of C-VATS (P > .05). Subgroup comparison of the rate of complications between lung resections versus other intrathoracic procedures, lung cancer versus pneumothorax, and lung cancer versus other lung-only lesions did not show any significant differences between the groups. CONCLUSION: Performing S-VATS technique has shown superior postoperative outcomes over the C-VATS technique in the treatment of thoracic disorders. Substantial benefit was confirmed in terms of less postoperative pain, blood loss, drainage time, and postoperative hospital stay.
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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.021 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".