691 IS OPEN LEFT THORACO-ABDOMINAL ESOPHAGECTOMY A VIABLE OPTION IN THE ERA OF MINIMALLY INVASIVE ESOPHAGECTOMY?
Bibliographic record
Abstract
Abstract Left thoracoabdominal esophagectomy (LTE) facilitates complete resection of esophageal cancer particularly for bulky tumors, but there are concerns that this approach is associated with significant morbidity. The aim of the current study was to evaluate short-term and oncological outcomes of left thoracoabdominal esophagectomy compared to minimally invasive esophagectomy for cancer of the esophagus and gastroesophageal junction. Methods Prospectively entered esophagectomy databases from three high volume centers were reviewed for patients undergoing LTE or MIE 2012–2018. Patient demographics, tumour characteristics, operative outcomes, postoperative outcomes, and pathologic surrogates of oncologic efficacy (R0 resection rate, and number of resected lymph nodes) were compared. In total 844 patients were included in the study, LTE was applied in 654 (77.5%) patients, and MIE in 190 (22.5%) patients. Results LTE patients had more neoadjuvant treatment (LTE = 74.5%, MIE = 64.9%, P = 0.027). There was no difference in overall postoperative complications (LTE = 61.9%, MIE = 64.6%, P = 0.517), severe complications (Clavien Dindo >IIIa (LTE = 26.6%, MIE 26.5%, P = 0.982), pneumonia (LTE = 29.8%, MIE = 26.3%, P = 0.349), anastomotic leak (LTE = 7.7%, MIE = 9.9%, P = 0.348), or in-hospital mortality (LTE = 1.5%, MIE = 2.1%, P = 0.584). Median length of stay was 11 days after LTE vs. 8 days after MIE (P < 0.001). R0 resection rate was 92.4% and 95.6% respectively (P = 0.144). Median number of resected lymph nodes was 25 for LTE and 28 for MIE (P = 0.017). LTE had more node positive resections (LTE = 57.6%, MIE = 44.0%, P = 0.001). Conclusion LTE was used for tumors with greater lymph node burden in patients that were more likely to have received neoadjuvant treatment compared to MIE. MIE was associated with significantly shorter length of hospital stay, however postoperative morbidity and Clavien-Dindo scores were equal to that of MIE in this cohort.
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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 | 0.005 |
| 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".