Implication of ERBB2 as a Predictive Tool for Survival in Patients with Pancreatic Cancer in Histological Studies
Bibliographic record
Abstract
Pancreatic cancer will be positioned by the year 2030 as the second cause of oncological death after lung cancer. The pathophysiology of the most common variety, which involves the adenocarcinoma of the pancreas, represents one of the main challenges for current oncology to explain its tumorigenesis and create a targeted treatment. The tumor microenvironment, metastatic capacity, and lack of early diagnosis lead patients to present advanced stages at the time of diagnosis. Despite numerous efforts, little progress has been made in clinical outcomes and with respect to the improved survival of these patients. For this reason, in recent years, numerous diagnostic tests, treatments, and possible approaches in the fields of radiotherapy, chemotherapy, immunotherapy, and surgery have been developed to find a combination of methods that improves life expectancy in patients diagnosed with this disease. On the other hand, the scientific community has made numerous advances in the molecular bases of pancreatic cancer since several oncogenetic pathways have been described and the markers expressed by the tumor have proven to be useful in the prognosis of pancreatic adenocarcinoma. These molecular alterations allow the study of possible therapeutic targets that improve the prognosis of these patients, but even numerous tumor cell-individual interactions must be explained to understand the underlying pathophysiology causing the high mortality. Therefore, the purpose of our study is to examine the expression of markers such as EGFR, Cyclin D1, andCDK4 in order to find a relationship with the possible long-term prognostic factors of patients affected by pancreatic ductal adenocarcinoma. Our results show that there is a prognostic role for ErbB2, EGFR, beta catenin, cyclin D1, and CDK4. Of these, we highlight the clinical importance of ErbB2 in the survival rates of patients who overexpress this component.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".