Comparing immediate postoperative outcomes of different VATS approaches for anatomical lung resection: a single-centre retrospective study
Bibliographic record
Abstract
Background Video-assisted thoracic surgery (VATS) can be performed through 1 or more intercostal or subxiphoid ports. The aim of this study was to evaluate whether number and location of ports had an impact on early perioperative outcomes and postoperative pain after anatomical lung resection (ALR). Methods A search of the departmental electronic database identified all patients who underwent VATS ALR between June 2018 and June 2019. We stratified patients according to the surgical approach: 2-port VATS, 3-port VATS, and subxiphoid VATS. We extracted demographic and clinicopathologic data. We used univariate analysis with unpaired t tests and χ2 tests to compare these variables between the subgroups. Results We included 201 patients in the analysis. When patients were stratified by surgical approach, there was no difference in terms of age, disease load, length of surgery, postoperative complications, duration of pleural drainage, and length of hospital stay. Postoperative pain and morphine equivalent usage were also comparable between the groups. According to these results, number and location of VATS ports seemingly has no clinical impact on early postoperative outcomes. Limitations of the study include its retrospective nature, small sample size, and short follow-up interval. Conclusion Our results suggest that incision location and the number of VATS ports is not associated with differences in the incidence of perioperative complications or postoperative pain. Given the limitations described above, further studies with longer follow-up intervals are required to explore the lasting impact of this surgical approach on quality of life.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".