Treatment of intrathoracic anastomotic leak by nose fistula tube drainage after esophagectomy for cancer
Bibliographic record
Abstract
Esophageal anastomotic leak remains a lethal complication after esophagectomy for cancer. The aim of the present study is to describe an effective new management, nose fistula tube drainage (NFTD), to treat postoperative intrathoracic leaks. From July 2003 to August 2009, 41 of 4132 patients (0.99%) requiring transthoracic esophagectomy for esophageal and cardiac carcinoma had developed an intrathoracic esophageal anastomotic leak in our hospital as well as another three patients with similar conditions from other hospitals, excluding three patients with gastric necrosis (two) and tracheo-esophageal fistula (one); 23 patients were treated by NFTD, and the remaining 18 patients were treated by conventional chest tube drainage (CCTD). Clinical records of these patients were reviewed and analyzed, including the healing of the leak, mortality, and morbidity. In the NFTD group, 4 patients (17.4%) died, 1 patient (4.3%) required reoperation, and 18 patients (78.3%) healed. However, in the CCTD group, 3 patients (16.7%) died, 1 patient (5.5%) required reoperation, and 14 patients (77.8%) healed. As compared with the CCTD group, patients of the NFTD group had a shorter intensive care course (11.95 vs 33.62 days, P= 0.01) and hospital stay (39.74 vs 77.54 days, P= 0.02). Although this novel NFTD management did not significantly decrease mortality when compared with CCTD, it could gain more effective drainage than CCTD and eventually shorten hospital stay.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".