Predicting the stages of PDAC using non-invasive method
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) is a highly lethal malignancy in which early and accurate staging is paramount for effective therapeutic decision-making and prognostication. Traditionally, the prediction of PDAC staging has depended heavily on invasive methods such as CT scans and tissue biopsies. These methods can be costly, time-consuming, and distressing for patients [1]. Recently, focus has shifted towards the development of non-invasive detection methods utilizing Machine Learning. Research conducted by the Barts Cancer Institute[2], which investigated the early detection of PDAC using bio-markers such as CA19-9, LYVE1, REG1B, REG1A, TFF1, and creatinine, has shown promising results. Our study is a subsequent investigation that relies on the same open-access data-set comprising 590 participants. It aims to first ascertain the previous work of researchers related to early detection of PDAC. Second, to investigate the possibility of utilizing the same bio-markers to predict the stage of PDAC, this differs our work from previous work. We have tried out several machine learning algorithms. Our results for the first problem, early prediction of PDAC are very promising - we achieved an accuracy of 87%, 84% and 78% for precision and recall respectively utilizing XGBoost. For the second problem, predicting the stage of PDAC, MLP neural networks yielded an accuracy of 70% with an AUC of 79%. These results are promising and better than the base models, which opens a new area for future works. Based on our work, we propose the establishment of a certified pancreatic bio-marker platform.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.008 | 0.004 |
| 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