A Multicentre Prospective Cohort Study to Identify High-Risk Transient Ischemic Attack/Minor Stroke Patients Benefitting from Echocardiography
Bibliographic record
Abstract
BACKGROUND: We aimed to derive a clinical decision rule to identify patients with transient ischemic attack (TIA) or minor stroke most likely to benefit from echocardiography. METHODS: This multicentre prospective cohort study enrolled adults diagnosed with TIA/minor stroke in the emergency department who underwent echocardiograms within 90 days, from 13 Canadian academic emergency departments from October 2006 to May 2017. Our outcome was clinically significant echocardiogram findings. RESULTS: In 7149 eligible patients, a clinically significant finding was found in 556 (7.8%). There were a further 2421 (33.9%) with potentially significant findings. History of heart failure (adjusted odds ratio [OR], 3.9) or coronary artery disease (OR, 2.7) were the factors most strongly associated with clinically significant echocardiogram findings, whereas young age, male sex, valvular heart disease, and infarct (any age) on neuroimaging were modestly associated (OR, 1.3-1.9). The model combining these predictors into a score (range: 0-15), had a C-statistic of 0.67 (95% confidence interval [CI], 0.65-0.70). A cut point of 6 points or more classified 6.6% of cases as high likelihood, defined as > 15% for clinically significant echocardiogram findings. CONCLUSIONS: Echocardiography is a very useful test in the investigations of patients with TIA/minor stroke. We identified high-risk clinical features-combined to create a clinical decision rule-to identify which patients with TIA/minor stroke are likely to have clinically significant echocardiogram findings requiring an immediate change in management. These patients should have echocardiography prioritized, whereas others may continue to have echocardiography conducted in a less urgent fashion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".