Oesophagectomy following noncurative endoscopic resection for oesophageal carcinoma: does interval matter?
Bibliographic record
Abstract
OBJECTIVES: Oesophagectomy was always recommended after noncurative endoscopic resection (ER). And the optimal time interval from ER to oesophagectomy remains unclear. This study was to explore the effect of interval on pathologic stage and prognosis. METHODS: We included 155 patients who underwent ER for cT1N0M0 oesophageal cancer and then received subsequent oesophagectomy from 2009 to 2019. Overall survival and disease-free survival (DFS) were analysed to find an optimal cut-off of interval from ER to oesophagectomy. In addition, pathologic stage after ER was compared to that of oesophagectomy. Logistic regression model was built to identify risk factors for pathological upstage. RESULTS: The greatest difference of DFS was found in the groups who underwent oesophagectomy before and after 30 days (P = 0.016). Among total 155 patients, 106 (68.39%) received oesophagectomy within 30 days, while 49 (31.61%) had interval over 30 days. Comparing the pathologic stage between ER and oesophagectomy, 26 patients had upstage and thus had worse DFS (hazard ratio = 3.780, P = 0.042). T1b invasion, lymphovascular invasion and interval >30-day group had a higher upstage rate (P = 0.014, P < 0.001 and P < 0.001, respectively). And they were independent risk factors for pathologic upstage (odds ratio = 3.782, 4.522 and 2.844, respectively). CONCLUSIONS: It was the first study exploring the relationship between time interval and prognosis in oesophageal cancer. The longer interval between noncurative ER and additional oesophagectomy was associated with a worse DFS, so oesophagectomy was recommended performed within 1 month after ER. Older age, T1b stage, lymphovascular invasion and interval >30 days were significantly associated with pathologic upstage, which is related to the worse outcome too.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".