88 Does CTA increase overdiagnosis through incidental findings in comparison to plain CT?
Bibliographic record
Abstract
Introduction Incidental findings are those which are not clinically significant for the patient but have the potential to cause worry and stress for the patient, as well as additional strain on the healthcare system through unnecessary (and potentially harmful) downstream testing and intervention. The effect of incidental findings is important to consider in the workup of patients with suspected acute ischemic stroke (AIS) and transient ischemic attack (TIA) since up to 30% of these cases are ultimately found to be non-ischemic mimics.1 Various forms of computed tomography (CT) are available for initial investigation of AIS and TIA. These modalities include non-contrast CT (NCCT), CT angiography head and neck (CTA), and CT perfusion (CTP). When choosing between modalities, physicians need to balance the need to accurately diagnose AIS/TIA against the potential for incidental findings on imaging. Objectives The primary objective was to characterize the balance between diagnostic yield and incidental findings between different neuroimaging strategies among patients presenting to urban academic emergency departments (ED)s with symptoms of AIS or TIA undergoing investigation. Methods In this cross-sectional study, we analyzed anonymized records for patients presenting to adult EDs in 2019 with a triage complaint of suspected stroke or TIA. Diagnostic imaging (DI) modalities were recorded, including non-contrast computed tomography (NCCT) and CT angiography (CTA). Other modalities were excluded from analysis because the number of these scans within our sample size was too few. DI reports were reviewed and coded into 3 categories based on a prespecified list of expert-consensus clinically significant neuroimaging Findings 1) significant findings (requiring immediate or urgent follow-up clinical action), 2) incidental findings (not meeting criteria for clinical significance), and 3) no abnormalities. The diagnostic yield was defined as the percentage of scans which produced significant findings. Standard descriptive statistics were performed. A two-sided p-value of 0.05 was considered significant. Results Among 551 included patients, 20% received NCCT alone and 80% received combined NCCT+CTA imaging. The diagnostic yield of NCCT alone for clinically significant findings was 6% compared to 24% in those who received NCCT+CTA (p<0.01). The proportion of incidental findings was non-significantly higher in the combined NCCT+CTA group as compared to the NCCT only group (36% vs 28% respectively, p=0.10). Conclusions In this study of patients presenting with suspected stroke or TIA, an NCCT+CTA neuroimaging strategy demonstrated a higher diagnostic yield than NCCT alone with an associated non-significantly increased proportion of incidental findings. This data can inform decision-making around neuroimaging in suspected AIS and TIA. Reference Hand P, Kwan J, Lindley R, et al. Distinguishing between stroke and mimic at the bedside: the brain attack study. Stroke 2006;37:769–775.
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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 | 0.009 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".