Open left thoracoabdominal esophagectomy a viable option in the era of minimally invasive esophagectomy
Bibliographic record
Abstract
Minimally invasive surgical technique has become standard at many institutions in esophageal cancer surgery. In some situations, however other surgical approaches are required. Left thoracoabdominal esophagectomy (LTE) facilitates complete resection of esophageal cancer particularly for bulky distal esophageal tumors, but there are concerns that this approach is associated with significant morbidity. Prospectively entered esophagectomy databases from three high-volume centers were reviewed for patients undergoing LTE or MIE 2009-2019. Patient demographics, tumor characteristics, operative outcomes, postoperative outcomes, and pathologic surrogates of oncologic efficacy (R0 resection rate, and number of resected lymph nodes) were compared. In total 915 patients were included in the study, LTE was applied in 684 (74.8%) patients, and MIE in 231 (25.2%) patients. LTE patients had more locally advanced tumor stage and received more neoadjuvant treatment. Patients treated with MIE had more comorbidities. The results showed no difference in overall postoperative complications (LTE = 61.7%, MIE = 65.7%, P = 0.289), severe complications (Clavien-Dindo ≥IIIa (LTE = 25.9%, MIE 26.8%, P = 0.806)), pneumonia (LTE = 29.0%, MIE = 24.7%, P = 0.211), anastomotic leak (LTE = 7.8%, MIE = 11.3%, P = 0.101), or in-hospital mortality (LTE = 2.6%, MIE = 3.5%, P = 0.511). Median number of resected lymph nodes was 24 for LTE and 25 for MIE (P = 0.491). LTE was used for more advanced tumors in patients that were more likely to have received neoadjuvant treatment compared with MIE, however postoperative morbidity, mortality, and oncologic outcomes were equivalent to that of MIE in this cohort. In conclusion open resection with left thoracoabdominal approach is a valid option in selected patients when performed at high-volume esophagectomy centers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".