Imaging for Screening/Surveillance of Pancreatic Cancer: A Glimpse of Hope
Bibliographic record
Abstract
Take-home points• There are emerging opportunities in the early detection of Pancreatic ductal adenocarcinoma (PDAC) with improved outcomes in certain highrisk individuals using imaging surveillance with MRI/endoscopic ultrasound.Still, there remains a challenge regarding the feasibility of PDAC screening in general population where most PDACs occur.• Surveillance with MRI is widely performed for patients with branch-duct intraductal papillary mucinous neoplasms (BD-IPMNs) although present imaging strategies may not be cost-effective.Further studies are needed to define a group that would benefit from a more intensive surveillance and another group that would not need surveillance by combining imaging and clinical/genetic features.• Early-stage PDAC can be subtle on imaging, obscured by coexistent entities such as chronic pancreatitis or IPMNs.Focal pancreatitis can be misdiagnosed as PDAC.Therefore, screening for PDACs should be ideally performed in centers with high-volume pancreatic MRI and imaging expertise.
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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.013 | 0.029 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".