Lymph Node Harvest After Proctectomy for Invasive Rectal Adenocarcinoma Following Neoadjuvant Therapy
Bibliographic record
Abstract
PURPOSE: Recent reports indicate that neoadjuvant therapy significantly reduces the lymph node harvest of rectal cancer. The aim of this study was to interpret the lymph node harvest in this setting based on the primary tumor response. METHODS: All patients undergoing proctectomy were included. Three variables were used as indicators of primary tumor response: ypT stage, tumor size, and tumor regression grade. RESULTS: From 1998 to 2007, 237 patients were identified: 157 in the neoadjuvant therapy group and 80 in the nonneoadjuvant therapy group. Neoadjuvant therapy significantly reduced the number of lymph nodes harvested (P = 0.011). Compared with the nonneoadjuvant group, there were significantly fewer lymph nodes in the neoadjuvant early T stage group (P = 0.001), small tumor size group (P = 0.003), and low tumor regression grade group (P < 0.001). However, there was no significant difference between the nonneoadjuvant group and the neoadjuvant advanced T stage (P = 0.664), large tumor (P = 0.815), and high tumor regression grade groups (P = 0.566). CONCLUSION: The current standard of lymph node harvest should be applied to patients with poorly responding primary tumors after neoadjuvant therapy. However, a new standard may be necessary to define the adequate number of lymph nodes for tumors that respond well to neoadjuvant therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".