Noncoding RNAs as potential biomarkers to predict the outcome in pancreatic cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC), a common digestive system cancer, is highly malignant and has a poor disease outcome. Currently, all available examination and detection methods cannot accurately predict the clinical outcome. Therefore, it is extremely important to identify novel molecular biomarkers for personalized medication and to significantly improve the overall outcome. The "noncoding RNAs" (ncRNAs) are a group of RNAs that do not code for proteins, and they are categorized as structural RNAs and regulatory RNAs. It has been shown that microRNAs and long ncRNAs function as regulatory RNAs to affect the progression of various diseases. Many studies have confirmed a role for ncRNAs in the progression of PDAC during the last few years. Because of the significant role of ncRNAs in PDAC, ncRNA profiling may be used to predict PDAC outcome with high accuracy. This review comprehensively analyzes the value of ncRNAs as potential biomarkers to predict the outcome in PDAC and the possible mechanisms thereof.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it