P72 IS OPEN LEFT THORACO-ABDOMINAL ESOPHAGECTOMY A VIABLE OPTION IN THE ERA OF MINIMALLY INVASIVE ESOPHAGECTOMY?
Bibliographic record
Abstract
Abstract Aim To evaluate short-term and oncological outcomes of left thoracoabdominal esophagectomy compared to minimally invasive esophagectomy for cancer of the esophagus and gastroesophageal junction Background and Methods 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. Prospectively entered esophagectomy databases from two high volume North American centers were reviewed for patients undergoing LTE or MIE in the 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. Results In total 247 patients were included in the study, LTE was applied in 170 (68.8%) patients, and MIE in 77 (31.2%) patients. LTE patients had more neoadjuvant treatment (LTE=78.2%, MIE=34.2%, P<0.001). There was no difference in overall postoperative complications (LTE=56.9%, MIE=55.0%, P=0.799), severe complications (Clavien Dindo>2 - LTE=26.1%, MIE17.0%, P=0.184), pulmonary complications (LTE=31.9%, MIE=20.0%, P=0.085), pneumonia (LTE=15.2%, MIE=13.6%, P=0.768), anastomotic leak (LTE=7%, MIE=10%, P=0.396), or postoperative mortality (LTE=0%, MIE=1.3%, P=0.140). Median length of stay was 7 days in both groups. R0 resection rate was 93.8% and 95.5% respectively (P=0.631). Median number of resected lymph nodes was 24 for LTE and 22 for MIE (P=0.226). LTE had more stage II-IV tumors (LTE=67.8%, MIE=40.7%, P<0.001), and more node positive resections (LTE=52.5%, MIE=31.4%, P=0.003). Conclusion LTE was used for larger tumors with greater lymph node burden in patients that were more likely to have received neoadjuvant treatment compared to MIE. Despite this the postoperative morbidity was equal to that of MIE, with no difference in short-term or oncological results 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 | 0.004 |
| 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) | 0.003 | 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".