Exploring the differences between early-onset gastric cancer and traditional-onset gastric cancer
Bibliographic record
Abstract
Background: The current study sought to explore the potential clinical, epidemiological and genetic differences between early-onset gastric cancer (E-gastric cancer: defined as 20–39 years) and traditional-onset gastric cancer (T-gastric cancer: defined as ≥40 years). Methods: Datasets from the following sources were searched: Surveillance, Epidemiology and End Results database [2000–2014], Behavioral Risk Factor Surveillance Survey and the cancer genome atlas (TCGA). Clinicopathological characteristics, trends, and genetic findings were compared between E-gastric cancer and T-gastric cancer. Moreover, correlations with relevant risk factors were sought after. Results: A total of 95,323 gastric cancer patients were identified in the period from 2000 to 2014. While T-gastric cancer was decreasing during the study period (−1.4; P<0.05), E-gastric cancer was stable during the study period. E-gastric cancer is less prevalent in males (51.1% vs. 61.0%; P<0.0001), and white patients (68.9% vs. 71.4%; P<0.0001). E-gastric cancer patients usually present with poorly differentiated histology (55.3% vs. 48.0%; P<0.0001) as well as more aggressive histological subtypes (e.g., diffuse histology or linitis plastica). No difference can be detected with regards to risk factor correlations between E-gastric cancer and T-gastric cancer. Only four patients with E-gastric cancer were available in the provisional TCGA dataset at the time of the study. Conclusions: E-gastric cancer is a potentially distinct disease entity with specific clinicopathological and trend patterns compared to conventional T-gastric cancer. Further studies are needed to explore the potential etiologic basis as well as to investigate the clinical consequences of this distinction. The impact of this distinction on minority populations requires further assessment as well.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".