Advances in diagnosis, treatment and palliation of pancreatic carcinoma: 1990-2010
Bibliographic record
Abstract
Several advances in genetics, diagnosis and palliation of pancreatic cancer (PC) have occurred in the last decades. A multidisciplinary approach to this disease is therefore recommended. PC is relatively common as it is the fourth leading cause of cancer related mortality. Most patients present with obstructive jaundice, epigastric or back pain, weight loss and anorexia. Despite improvements in diagnostic modalities, the majority of cases are still detected in advanced stages. The only curative treatment for PC remains surgical resection. No more than 20% of patients are candidates for surgery at the time of diagnosis and survival remains quite poor as adjuvant therapies are not very effective. A small percentage of patients with borderline non-resectable PC might benefit from neo-adjuvant chemoradiation therapy enabling them to undergo resection; however, randomized controlled studies are needed to prove the benefits of this strategy. Patients with unresectable PC benefit from palliative interventions such as biliary decompression and celiac plexus block. Further clinical trials to evaluate new chemo and radiation protocols as well as identification of genetic markers for PC are needed to improve the overall survival of patients affected by PC, as the current overall 5-year survival rate of patients affected by PC is still less than 5%. The aim of this article is to review the most recent high quality literature on this topic.
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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 | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".