Extended Lymphadenectomy for Gastric Cancer in the Neoadjuvant Era: Current Status, Clinical Implications and Contentious Issues
Bibliographic record
Abstract
Despite its decreasing incidence, gastric cancer remains an important global healthcare problem due to its overall high prevalence and high mortality rate. Since the MAGIC and FNLCC/FFCD trials, the neoadjuvant chemotherapy has been recommended throughout Europe in gastric cancer. Potential benefits of preoperative treatments include a higher rate of R0 resection achieved by downstaging the primary tumor, a likely effect on micrometastases and isolated tumor cells in the lymph nodes, and, as a result, improved cancer-related survival. Nevertheless, distortion of anatomical planes of dissection, interstitial fibrosis, and sclerotic tissue changes may increase surgical difficulty. The collection of at least twenty-five lymph nodes after neoadjuvant therapy would seem to ensure removal of undetectable node metastasis and reduce the likelihood of locoregional recurrence. It is not what you take but what you leave behind that defines survival. Therefore, para-aortic lymph node dissection is safe and effective after neoadjuvant chemotherapy, in both therapeutic and prophylactic settings. In this review, the efficacy of adequate lymph node dissection, also in a neoadjuvant setting, has been investigated in the key studies conducted to date on the topic.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".