Abstract TP220: TIA Referral Influences Delay of Evaluation in TIA Clinic
Bibliographic record
Abstract
Background: Transient ischemic attacks and minor stroke are vascular emergencies associated with a high risk of brain infarction within the first hours after onset. When available those patients may be evaluated and managed in a TIA clinic. Several therapeutic strategies are currently tested within 12 hrs after TIA/minor stroke onset. We aim to investigate the factors associated with a delay of evaluation longer than 12 hours in our TIA clinic. Subjects and Methods: Subjects are consecutive patients evaluated in an academic center TIA clinic during 2 years. Briefly patients were evaluated by a certified stroke neurologist according to 2009 AHA recommendations including MRI, vessel imaging, blood tests, EKG and when needed TTE/TEE. Referring pathways were dichotomized between office based physicians (General practitioners, others:cardiologists, ophtalmologists, ...) and Emergency medical service (local and from other hospital with no neurologists). Univariate and multivariate logistic regression were performed. Results: 354 patients were evaluated in 2 years. Mean (+/- SD) age was 61 YO (18), Median (IQR) ABCD2 score was 3 (2-4); median (IQR) delay from onset to evaluation was 8 hours (4-48), 59% of patients did arrive in TIA clinic within 12 hours after onset. 52% were referred by an office based physician (36% general practitioner and 16% others) vs. 48% by ED (32%,local ED and EMS 16% other ED) Univariate analysis showed that ABCD2 score<4 and office based physician referral were associated with a delay>12 hrs. There was no relationships with other factors such as risk factors, previous history of stroke or recurrent TIA. Office based physician referral was the only independent factor associated with a delay of evaluation > 12 hrs OR 5.7, (95%CI:3.5-9.3, p<0.0001) after multivariate logistic regression. Conclusion: Direct referral from ED increases the rate of patients starting their evaluation in TIA clinic within 12 hrs after onset.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".