Prognostic value of lymph node ratio in survival of patients with locally advanced rectal cancer
Bibliographic record
Abstract
BACKGROUND: The lymph node ratio (LNR) has been shown to be an important prognostic factor in patients with gastric, breast, pancreatic and colorectal cancer. We investigated the prognostic impact of the LNR in addition to TNM classification in patients with locally advanced rectal cancer. METHODS: We retrospectively analyzed patients who underwent curative resection for locally advanced rectal cancer between July 2005 and December 2010. We determined the LNR cutoff value using a receiver operating characteristic curve. The Kaplan-Meier method was used to estimate survival curves, while Cox regression analyses were used to evaluate the relationship between LNR and survival. RESULTS: We included 180 patients aged 28-83 years with median follow-up of 41.8 months. The median number of lymph nodes examined and lymph nodes involved were 11.5 and 4, respectively, and the median LNR was 0.366. An LNR of 0.19 (19%) was the cutoff point to separate patients with regard to median overall survival. Median overall survival was 64.2 months for patients with an LNR of 0, 59.1 for an LNR of 0.19 or less and 37.6 for an LNR greater than 0.19 (p = 0.004). The median disease-free survival was 32.9 months for patients with an LNR of 0, 30.4 for an LNR of 0.19 or less and 17.8 for an LNR greater than 0.19 (p = 0.002). CONCLUSION: Our results suggest that LNR should be considered an additional prognostic factor in patients with locally advanced rectal 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 | 0.000 | 0.003 |
| 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".