Totally minimally invasive esophagectomy versus hybrid minimally invasive esophagectomy: systematic review and meta-analysis
Bibliographic record
Abstract
Minimally invasive esophagectomy is increasingly performed for the treatment of esophageal cancer, but it is unclear whether hybrid minimally invasive esophagectomy (HMIE) or totally minimally invasive esophagectomy (TMIE) should be preferred. The objective of this study was to perform a meta-analysis of studies comparing HMIE with TMIE. A systematic literature search was performed in MEDLINE, Embase, and the Cochrane Library. Articles comparing HMIE and TMIE were included. The Newcastle-Ottawa scale was used for critical appraisal of methodological quality. The primary outcome was pneumonia. Sensitivity analysis was performed by analyzing outcome for open chest hybrid MIE versus total TMIE and open abdomen MIE versus TMIE separately. Therefore, subgroup analysis was performed for laparoscopy-assisted HMIE versus TMIE, thoracoscopy-assisted HMIE versus TMIE, Ivor Lewis HMIE versus Ivor Lewis TMIE, and McKeown HMIE versus McKeown TMIE. There were no randomized controlled trials. Twenty-nine studies with a total of 3732 patients were included. Studies had a low to moderate risk of bias. In the main analysis, the pooled incidence of pneumonia was 19.0% after HMIE and 9.8% after TMIE which was not significantly different between the groups (RR: 1.46, 95% CI: 0.97-2.20). TMIE was associated with a lower incidence of wound infections (RR: 1.81, 95% CI: 1.13-2.90) and less blood loss (SMD: 0.78, 95% CI: 0.34-1.22) but with longer operative time (SMD:-0.33, 95% CI: -0.59--0.08). In subgroup analysis, laparoscopy-assisted HMIE was associated with a higher lymph node count than TMIE, and Ivor Lewis HMIE was associated with a lower anastomotic leakage rate than Ivor Lewis TMIE. In general, TMIE was associated with moderately lower morbidity compared to HMIE, but randomized controlled evidence is lacking. The higher leakage rate and lower lymph node count that was found after TMIE in sensitivity analysis indicate that TMIE can also have disadvantages. The findings of this meta-analysis should be considered carefully by surgeons when moving from HMIE to TMIE.
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 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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".