Local recurrence after total or subtotal esophagectomy for esophageal cancer.
Bibliographic record
Abstract
Cancer recurrence is a common problem after esophagectomy for esophageal cancer. Local recurrence is especially problematic because it often negates the palliative benefit of esophagectomy. We conducted a retrospective review to assess the effect of extent of esophageal resection (subtotal or total esophagectomy) on local cancer recurrence. Seventy-four consecutive patients with esophageal cancer underwent esophagectomy at our institution over a four-year period. Their charts were reviewed retrospectively and data was collected on age, gender, histology, stage, tumor location, operation, resection margin status, anastomotic leaks, operative mortality, adjuvant therapy, cancer survival, and local recurrence. Total esophagectomy was done in 19 patients (transhiatal - 3; McKeown - 16) and subtotal esophagectomy was done in the other 55 patients (Lewis - 25; left thoracoabdominal - 30). The two groups were similar with respect to age, gender, histology, stage, anastomotic leaks, operative mortality, adjuvant therapy, and overall survival. Resection margins were positive for residual tumor in 2 out of 19 (11%) total esophagectomies and 9 out of 55 (16%) subtotal esophagectomies (p=0.42). Local recurrence occurred in 3 of 19 (16%) patients treated with total esophagectomy and 23 out of 55 (42%) patients treated with subtotal esophagectomy (p=0.04). We conclude that total esophagectomy is associated with fewer local cancer recurrences than subtotal esophagectomy. We, therefore, recommend total esophagectomy for the surgical treatment of esophageal cancer.
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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 | 0.003 |
| 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) | 0.001 | 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".