A Contemporary Review of the Treatment Landscape and the Role of Predictive and Prognostic Biomarkers in Pancreatic Adenocarcinoma
Bibliographic record
Abstract
Pancreatic cancer continues to represent one of the leading causes of cancer-related morbidity and mortality in the developed world. Over the past decade, novel systemic therapy combination regimens have contributed to clinically meaningful and statistically significant improvements in overall survival as compared to conventional monotherapy. However, the prognosis for most patients remains guarded secondary to the advanced stages of disease at presentation. There is growing consensus that outcomes can be further optimized with the use of predictive and prognostic biomarkers whereby the former can be enriching for patients who would benefit from therapies and the latter can inform decision-making regarding the need and timing of advanced care planning. One of the challenges of current biomarkers is the lack of standardization across clinical practices such that comparability between jurisdictions can be difficult or even impossible. This inconsistency can impede widespread implementation of their use. In this review article, we provide a comprehensive overview of the contemporary treatment options for pancreatic cancer and we offer some insights into the existing landscape and future directions of biomarker development for this disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| 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) | 0.003 | 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".