Prevalence and significance of extravascular incidental findings on computed tomographic angiography and magnetic resonance angiography
Bibliographic record
Abstract
Computed tomographic angiography (CTA) and magnetic resonance angiography (MRA) are routinely used to evaluate patients with vascular disease. They have the ability to detect unexpected non-vascular pathology. The purpose of this study was to determine the prevalence and significance of extravascular incidental findings in patients undergoing CTA or MRA. A retrospective review of 737 patients who underwent CTA and 184 patients who underwent MRA during a five-year period was performed. Incidental findings were classified as low, moderate or high significance findings. For patients with high significance extravascular findings, assessment of the rates of appropriate follow-up was conducted. Among the CTA patients, 539 (73.1%) had incidental findings. Low, moderate and high significance findings were discovered in 514 (69.7%), 95 (12.9%) and 41 (5.6%) patients, respectively. Twenty (48.8%) patients with high significance findings received appropriate follow-up investigations. Among the MRA patients, 95 (51.6%) had extravascular findings. Low, moderate and high significance findings were present in 80 (43.5%), 27 (14.7%), and 3 (1.6%) patients, respectively. Two (66.7%) patients with high significance findings were properly followed up. In conclusion, incidental findings on CTA and MRA are very common. A small percentage of these findings could be serious and were not all adequately followed-up in our study population. Referring physicians should be aware of the potential for serious incidental findings and manage them appropriately.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".