Prognostic Factors of Patients With Transmural Advanced Gastric Carcinoma
Bibliographic record
Abstract
BACKGROUND: The purpose of this study is to evaluate perioperative morbidity, mortality and the prognostic factors that influence survival of the patients with transmural advanced gastric carcinoma after curative surgical therapy. METHODS: Fifty patients with transmural advanced gastric adenocarcinoma underwent curative resection in our clinic. The records of the patients were reviewed and the prognostic factors such as age, gender, location and size of the tumor, type of surgery, blood transfusion, depth of tumor invasion, lymph node metastases, stage of the disease, grading, vascular invasion, lymph vessel invasion, characteristics of the tumor according to Lauren's classification, and lymph node ratio were evaluated by using statistical methods. RESULTS: In a total of 12 patients (24%) major morbidities developed, and five patients (10%) died. The overall survival rate was 48% at 1 year, 31% at 3 years, and 19% at 5 years. Lymph node metastases (P = 0.03), lymph vessel invasion (P = 0.001), blood transfusion (P = 0.021), and lymph node ratio (P = 0.006) were the prognostic features identified by univariate analysis. Among the multiple significant prognostic factors in the univariate analysis only one factor, lymph node ratio, proved to be independently significant in the multivariate analysis (RR: 4.47). CONCLUSIONS: Our data showed that we can expect a good survival for patients with a lymph node ratio less than 0.2.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".