Clinicopathological characteristics and prognostic analysis of Lauren classification in gastric adenocarcinoma in China
Bibliographic record
Abstract
BACKGROUND: According to the Lauren classification, gastric adenocarcinomas are divided into diffuse and intestinal types. The causative attribution explaining the dismal prognosis of diffuse-type remains unknown. METHODS: We examined the archive of 1000 patients with gastric adenocarcinomas who received radical gastrectomy in our center and assessed the effect of the Lauren classification on survival in a multivariate approach. Moreover we compared the variation of clinical features between the diffuse-type and intestinal-type and explored the contributing factors for the prognostic difference. RESULTS: There were 805 resectable patients for the final analysis. Diffuse-type comprised of 48.7% in the gastric carcinoma in our group and showed poorer prognosis than intestinal-type (P=0.013). Multivariate analysis revealed that independent prognostic factors for gastric carcinoma patients were T stage (P<0.001), N stage (P<0.001) tumor size (P<0.001) and Lauren classification (P=0.003). For the clinical features, diffuse-type was significantly associated with younger age (p<0.001), female preponderance (p <0.001), distal location (P<0.001), advanced pT (p < 0.001), advanced pN (p < 0.001) and advanced TNM stage (p = 0.027). CONCLUSIONS: Diffuse type adenocarcinoma carries a worse prognosis that may be partially explained by the tendency of this subtype to present at more advanced T and N stage. However, Lauren classification has prognostic significance that is independent of T and N stage as well as other prognostic variables based on the multivariate cox analysis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".