Advances in Cardiac Workup for Transient Ischemic Attack: Improving Diagnostic Yield and Reducing Recurrent Stroke Risk
Bibliographic record
Abstract
Transient ischemic attack (TIA) is a warning sign for an impending stroke, with a 10-20% chance of a stroke occurring within 90 days of the initial event. Current clinical practice for cardiac workup in TIA includes cardiac enzymes, with 12-lead electrocardiogram, transthoracic echocardiography, and 24-hour Holter monitoring. However, the diagnostic yield of these investigations is variable, and there is a need for better diagnostic approaches to increase the detection of cardiac abnormalities in a cost-effective way. This review article examines the latest research on emerging diagnostic tools and strategies and discusses the potential benefits and challenges of using these advanced diagnostic approaches in clinical practice. Novel biomarkers, imaging techniques, and prolonged rhythm monitoring devices have shown great promise in enhancing the diagnostic yield of cardiac workup in TIA patients. Echocardiography, Transcranial Doppler ultrasound, cardiac MRI, and cardiac CT are among the promising diagnostic tools being studied. We conclude the article with a suggested diagnostic algorithm for cardiac workup in TIA. Further research is necessary to enhance their usefulness and to outline future directions for research and clinical practice in this field.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".