Non-Cardiovascular Computed Tomography Incidental Findings in Patients Who Underwent Transaortic Valve Implantation Procedure
Bibliographic record
Abstract
BACKGROUND: Transcatheter aortic valve implantation (TAVI) is a new treatment option for patients with severe aortic stenosis. Pre-TAVI procedure workup includes computed tomography angiography (CTA) of the heart and aorta from aortic annulus to the iliofemoral arteries. Frequently, there are a number of incidental non-cardiac findings (INCFs) in pre-TAVI CTA. However, the frequency and clinical significance of these INCFs are unknown. The aim of our study was to investigate the prevalence of INCFs and their clinical significance. METHODS: This was a retrospective review of 67 patients who underwent dedicated pre-TAVI CTA from 2010 till 2015. Non-cardiovascular INCFs were classified according to their clinical significance into three categories. The first category includes findings that may require urgent treatment. The second category includes findings that need further follow-up. The third category includes incidental findings that require no further follow-up or recommendation. RESULTS: The total number of patients was 67, and the mean age was 73 ± 8 years. All patients had INCFs and the total number was 248. Of the patients, 69% had chest findings, 85% had abdominal findings, and 33% had musculoskeletal findings. Results based on categorical classification were as follows: 9%, 25%, and 66% of these 248 findings belong to the first category, the second category, and the third category, respectively. CONCLUSION: Non-cardiovascular INCFs are common in pre-TAVI CTA presumably due to increased age of such specific population. These findings have variable clinical significance and some of them might require acute treatment or additional evaluation, and should be managed properly taking into consideration patient's life expectancy and comorbidities.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".