Moving the Needle on Precision Medicine in Pancreatic Cancer
Bibliographic record
Abstract
The management of pancreatic ductal adenocarcinoma (PDAC) has posed a considerable challenge for decades, with incidence and mortality rates almost mirroring each other. Despite this, a deeper understanding of the complex biology inherent to PDAC has provided a roadmap for a more precise approach to treatment. PDAC deficient in homologous recombination repair and mismatch repair is a subgroup that should be identified in the clinic for a targeted approach. In addition, KRAS wild-type PDAC, occurring in approximately 10% of patients, is enriched in highly actionable alterations including fusions, underscoring the importance of integrative germline and somatic sequencing. Comprehensive sequencing efforts over the past decade have documented genomic- and transcriptomic-based classifiers, with the latter emerging as two main subtypes: the classical and basal-like, which are now being evaluated in clinical trials. Together with promising, innovative strategies to target KRAS mutations and their pleotropic effects, a new era of precision medicine in PDAC is on the horizon.
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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.012 | 0.024 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".