Inflammatory Cytokines and Radiotherapy in Pancreatic Ductal Adenocarcinoma
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) remains a therapeutic challenge in clinical oncology. Surgery is the only potentially curative treatment. However, the majority of PDAC patients present with locally advanced/unresectable or metastatic disease, where palliative multiagent chemotherapy is the first-line treatment with the therapeutic intent to delay progression and prolong survival. For locally advanced/unresectable pancreatic cancer patients who are treated with chemotherapy, consolidative radiotherapy in the form concurrent chemoradiation or stereotactic ablative radiotherapy improves locoregional control and pain/symptom control. To improve clinical outcomes of PDAC patients, there is a dire need for discoveries that will shed more light on the pathophysiology of the disease and lead to the development of more efficacious treatment strategies. Inflammatory cytokines are known to play a role in mediating tumor progression, chemoresistance, and radioresistance in PDAC. A PubMed search on published articles related to radiotherapy, inflammatory cytokines, and pancreatic cancer patients in the English language was performed. This article primarily focuses on reviewing the clinical literature that examines the association of inflammatory cytokines with clinical outcomes and the effects of radiotherapy on inflammatory cytokines in PDAC patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".