Bibliographic record
Abstract
Upper gastrointestinal (GI) tumors, including adenocarcinoma of the esophagus, stomach, pancreas, and biliary tree, have traditionally been difficult to treat with cytotoxic chemotherapeutic agents. There has been little drug development success in treating these cancers over the last 20 years, perhaps a reflection of a combination of the aggressive biology of these tumors, the void in effective and specific drug development for these varied tumors, and the lack of properly designed, biologically-based clinical trials. Recently, so called "targeted agents" have risen to the forefront in the care of cancer patients and have made strong impacts in many areas of oncology, particularly gastrointestinal stromal tumors (GIST), colon, breast, and lung cancers. Unfortunately, slow progress has been made using such agents in upper GI tumors. However, more recently, trials in some tumor types have demonstrated gains in progression free survival and overall survival. In this review, we discuss the drugs and pathways that have been most successful in the treatment of upper GI tumors and present the relevant data supporting their use for each tumor site. Additionally, we will explore a few novel pathways that may prove effective in the treatment of upper GI malignancies in the near future.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".