Oncologic outcomes in minimally invasive esophagectomy for esophageal carcinoma
Bibliographic record
Abstract
Abstract: Esophagectomy, with clear surgical margins and appropriate lymph node dissection, is the mainstay of curative intent treatment in esophageal cancer. Minimally invasive esophagectomy (MIE) including total MIE, hybrid and robotic approaches has become more frequent. Whether or not MIE offers the same oncologic outcomes as open esophagectomy (OE) is an unanswered question to be addressed in this review. A literature review was conducted using the search terms esophageal cancer, esophagectomy, outcomes, minimally invasive surgery, survival, lymph node assessment. Relevant studies were identified by two of the authors (NAD and GED). Studies reporting short terms outcomes consistently report decreased frequency of pneumonia, decreased blood loss and shorter length of stay. Margin status, appears to be similar while lymph node harvest is similar or better compared to open surgery. Overall survival (OS) is at least similar to open surgery but in several recent reports, survival after MIE is superior. Health-related quality of life (HRQOL) appears to be improved with MIE within the first 6 months but similar thereafter. MIE offers at least equivalent outcomes in terms of resection margins and lymph node harvest and long-term survival data. MIE offers earlier recovery from surgery with improved HRQOL within the first 6 months postoperatively.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".