Bibliographic record
Abstract
The incidence of adenocarcinoma of the pancreas has risen steadily over the past four decades. Since pancreatic cancer is usually diagnosed at an advanced stage and because of the lack of effective therapies, the prognosis of such patients is extremely poor. Despite advances in our understanding of the molecular biology of pancreatic cancer, the systemic treatment of this disease remains unsatisfactory. Conventional chemotherapy has not produced dramatic improvements in response rates or patient survival. New treatment strategies are clearly needed. This paper will review emerging therapies for pancreatic carcinoma. A deeper understanding of the molecular biology of cell growth and proliferation, as well as of neoplastic cell transformation, has led to advances in several areas, including the use of hormones and antihormones as adjuvant therapy; inhibition of tumour growth and metastasis by inhibitors of matrix metalloproteases and angiogenesis, and by small molecules, such as retinoids, which interfere with progression through the cell cycle; immunotherapy with monoclonal antibodies; disruption of intracellular signal transduction with farnesyltransferase inhibitors; and, finally, gene therapy with specifically designed vaccines.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".