Unveiling Therapeutic Targets for Esophageal Cancer: A Comprehensive Review
Bibliographic record
Abstract
Esophageal cancer is a highly aggressive and deadly disease, ranking as the sixth leading cause of cancer-related deaths worldwide. Despite advances in treatment, the prognosis remains poor. A multidisciplinary approach is crucial for achieving complete remission, with treatment options varying based on disease stage. Surgical intervention and endoscopic treatment are used for localized cancer, while systemic treatments like chemoradiotherapy and targeted drug therapy play a crucial role. Molecular markers such as HER2 and EGFR can be targeted with drugs like trastuzumab and cetuximab, and immunotherapy drugs like pembrolizumab and nivolumab show promise by targeting immune checkpoint proteins. Epigenetic modifications offer new avenues for targeted therapy. Treatment selection depends on factors like stage, tumor location, and patient health, with post-operative and rehabilitation care being essential. Early diagnosis, appropriate treatment, and supportive care are key to improving outcomes. Continued research is needed to develop effective targeted drugs with minimal side effects. This review serves as a valuable resource for clinicians and researchers dedicated to enhancing esophageal cancer treatment outcomes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".