Abstract TP267: Performance of Components of the Canadian TIA Score to Predict Acute InfarctionAmong Patients Managed in an Emergency Department Observation Unit
Bibliographic record
Abstract
Introduction: TIA risk stratification can help inform of impending stroke, but some scores lack sensitivity while others are impractical in the emergency department (ED) setting. The Canadian TIA Score has shown good discriminative ability at predicting early stroke recurrence and is designed to be applied in the ED, but awaits further validation. Objective: We assessed the performance components of the Canadian TIA Score in predicting diffusion weighted MRI (DWI) abnormalities and 30-day adverse events in a cohort of clinically suspected TIAs placed in an ED observation unit (OU). Methods: Patients in a large, urban, academic ED with suspected TIA deemed appropriate for the OU from 4/2013-7/2018 were included. Rates of acute infarct on DWI and adverse 30-day events were assessed. Logistic regression was performed to determine the odds of DWI-confirmed stroke from 12 of the 13 items on the weighted Canadian TIA Score (we did not collect platelet count); and for poor versus good health outcomes at 30-days. The AUC with 95% CIs were also calculated for significant models, and the The Youden index (J statistic) was determined to assess maximum effectiveness of diagnostic test across a range of cut-points. Results: Of 1208 patients admitted over the time period, 1097 had DWI performed (90.1%). Clinical features are described in Table 1 . The logistic regression model for the Canadian TIA Score predicting acute stroke on DWI was significant (Wald χ2 (1) = 36.6, p < 0.001), with an odds ratio of 1.27 (95%CI: 1.16, 1.33), and AUC = 0.65 (95%CI: 0.61, 0.69). In predicting acute stroke, the maximum J value = 0.20; Canadian rule score ≥ 4; sensitivity = 0.68, specificity = 0.52. Of 593 patients reached at 30-days, 510 (86%) reported no adverse events. Of the 83 who did not, 1 had a disabling stroke and 1 had died. Discussion: In this overall low-risk cohort of ED patients with suspected TIA managed in an OU, the Canadian TIA Score performed reasonably well at predicting acute DWI abnormalities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".