National Survey of Canadian Neurologists’ Current Practice for Transient Ischemic Attack and the Need for a Clinical Decision Rule
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Four percent to 10% of patients with transient ischemic attack (TIA) have a stroke or die within 1 week of their diagnosis. This national survey examined Canadian neurologists' current practice for managing TIA, the need for a clinical decision rule to identify high-risk patients, and the required sensitivity of such a rule. METHODS: We surveyed 650 neurologists registered in a national physician directory. We used a modified Dillman technique with a prenotification letter and up to 5 survey attempts using a mailed letter. Neurologists were asked 33 questions about demographics, current management of adult patients with TIA, if a clinical decision rule is required to identify high-risk patients with TIA for impending stroke/death, and the required sensitivity of this rule. RESULTS: We had a response rate of 49.8% (324 of 650). Respondents were 78.3% male and had a mean age of 50.3 years. Of respondents, 49.2% (95% CI: 45.3% to 53.1%) reported using an existing clinical tool to risk-stratify patients. Overall, 95.0% (95% CI: 93.3% to 96.7%) reported they would consider using a sensitive, validated clinical decision rule for risk-stratifying patients with TIA. The median required sensitivity of a rule was 92% (interquartile range, 90 to 95). CONCLUSIONS: We found that Canadian neurologists would use a highly sensitive clinical decision rule to risk-stratify patients with TIA. The median required sensitivity of 92% is higher than the high risk category of any existing tool. Our results indicate a clinical decision rule to predict high-risk TIA needs to be more sensitive than the currently available rules.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".