Lymph node harvest as a predictor of survival for colon cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
The number of lymph nodes found harboring metastasis can be impacted by the extent of harvest. Guidelines recommend 12 lymph nodes for adequate lymphadenectomy to predict long-term oncologic outcomes, yet different cut-offs remain unevaluated. The aim of this review was to determine cut-offs that may predict survival outcomes. Medline, Embase, and CENTRAL were systematically searched. Articles were included if they compared overall survival (OS) or disease-free survival (DFS) above and below a lymph node harvest cut-off. Studies solely examining rectal cancer or stage-IV disease were excluded. Pairwise meta-analyses using inverse variance random effects were performed. From 2587 citations, 20 studies with 854,359 patients (51.9% female, mean age: 68.9) were included, with 19 studies included in quantitative synthesis. A lymph node harvest cut-off of 12 predicted improved five-year OS (7 studies; OR 1.11, 95% CI 1.08–1.14, p<0.00001). A cut-off as low as 7 was associated with improved five-year OS (2 studies; OR 1.16, 95% CI 1.08–1.25, p<0.0001) and DFS (3 studies; OR 1.66, 95% CI 1.32–2.10, p<0.00001). All cut-offs greater than 12 demonstrated improved survival. A lymph node cut-off of 12 distinguishes differences in five-year oncologic outcomes. Contrarily, lymph node harvests other than 12 have not been rigorously studied and thus lack the statistical power to derive meaningful conclusions compared to the 12-lymph node cut-off. Nonetheless, it is possible that a lymph node harvest cut-offs less than 12 may be adequate in predicting long-term survival. Further prospective study evaluating cut-offs below 12 are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".