Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: This update reviews the epidemiology and surgical management, and the controversies of gastric adenocarcinoma. We provide the relevance of outcome data to surgical decision-making and discuss the application of gene-expression analysis to clinical practice. SUMMARY BACKGROUND DATA: Gastric cancer mortality rates have remained relatively unchanged over the past 30 years, and gastric cancer continues to be one of the leading causes of cancer-related death. Well-conducted studies have stimulated changes to surgical decision-making and technique. Microarray studies linked to predictive outcome models are poised to advance our understanding of the biologic behavior of gastric cancer and improve surgical management and outcome. METHODS: We performed a review of the English gastric adenocarcinoma medical literature (1980-2003). This review included epidemiology, pathology and staging, surgical management, issues and controversies in management, prognostic variables, and the application of outcome models to gastric cancer. The results of DNA microarray analysis in various cancers and its predictive abilities in gastric cancer are considered. RESULTS: Prognostic studies have provided valuable data to better the understanding of gastric cancer. These studies have contributed to improved surgical technique, more accurate pathologic characterization, and the identification of clinically useful prognostic markers. The application of microarray analysis linked to predictive models will provide a molecular understanding of the biology driving gastric cancer. CONCLUSIONS: Predictive models generate important information allowing a logical evolution in the surgical and pathologic understanding and therapy for gastric cancer. However, a greater understanding of the molecular changes associated with gastric cancer is needed to guide surgical and medical therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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 it