Clinical Manifestations in Patients with Alpha-Fetoprotein–Producing Gastric Cancer
Bibliographic record
Abstract
BACKGROUND: Patients with alpha-fetoprotein (afp)-producing gastric cancer have a high incidence of liver metastasis and poor prognosis. There is some controversy about clinical manifestations in these patients. METHODS: Our study enrolled patients who, before surgery, had gastric cancer with serum afp exceeding 20 ng/mL [afp>20 (n = 58)] and with serum afp 20 ng/mL or less [afp≤20 (n = 1236)]. Clinical manifestations were compared between the groups. RESULTS: Early gastric cancer was more frequent (30.1% vs. 4%) and advanced gastric cancer was less frequent (69.9% vs. 96%) in the afp≤20 group than the afp>20 group (p < 0.001). Liver and lymph node metastasis occurred less frequently in the afp≤20 group (4.4% vs. 27.6%, p < 0.001, and 60.7% vs. 91.4%, p < 0.001, respectively). The 1-, 3-, 5-, and 10-year survival rates of afp≤20 patients were 75.2%, 53.4%, 45.8%, and 34.6% respectively. The 1-, 3-, 5-, and 10-year survival rates of patients with afp greater than 20 ng/mL, but 300 ng/mL or less, were 46.7%, 28.9%, 17.8%, and 13.3% respectively. The 1-, 3-, and 5-year survival rates of patients with serum afp greater than 300 ng/mL were 15.4%, 7.7%, and 0% respectively. The independent predictors for survival time were afp concentration, age, peritoneal seeding, liver metastasis, lymph node metastasis, vascular invasion, TNM stage, curative surgery, serosal invasion, and Lauren classification. CONCLUSIONS: Patients with high serum afp had a high frequency of liver and lymph node metastasis and very poor prognosis. More aggressive management with multimodal therapy (for example, chemotherapy, radiotherapy) might be needed when treating such patients.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".