Bibliographic record
Abstract
Abdominal aortic aneurysms (AAAs) occur when a large blood vessel, the aorta, which supplies blood to the abdomen, pelvis, and legs, becomes exceedingly large. This can become dangerous as the aneurysm may rupture, and cause internal bleeding. Treatments for AAAs have become increasingly effective, and with proper detection, grafts may be used to surgically fix the aneurysm. Surgeons at The University of Tennessee Medical Center have seen a large variability in the survival and effectiveness of such grafts, and are working with Oak Ridge National Laboratory to help predict the success or failure of an AAA repair. The goal of this research was to analyze and integrate the results of a follow-up study on patients who have had repairs on abdominal aortic aneurysms. The analysis utilized text mining and statistical software. Radiological reports were analyzed initially using a text mining software. Documents clustered based on common words and phrases, and those relating to the occurrence of an endovascular leak (endoleak) were identified. These trends in the text were then tested for statistical significance. A contingency analysis showed a significant difference in endoleak occurrence in the populations with sigmoid diverticulosis and gallstones. Time points for the occurrence for endoleak were also plotted, and trends were identified. The results of this study provide a useful analysis of the patient dataset, and identify significant trends among patients with endoleaks after AAA repair. This study will contribute to the development of multi-modal mathematical models to predict the outcome of an abdominal aortic aneurysm repair.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".