Abstract 34: Improvement In Etiological Identification In Tia And Minor Stroke Using The Causative Classification Of Stroke.
Bibliographic record
Abstract
Background: classification of Transient Ischemic attacks (TIA) and minor stroke is challenging, as there is no classification systems developed specifically for the TIA and minor stroke patient population. Hypothesis: We hypothesize that the newly developed Causative Classification System (CCS) and the Atherosclerosis Small Vessel Disease Cardiac Source Other Source (ASCO) classification would reduce the proportion of patients classified as cause undetermined compared with The Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification in a large prospectively evaluated TIA and Minor stroke population. Methods: Using published algorithms for TOAST, CCS, and ASCO, a single rater classified the etiology in patients presenting with a high-risk TIA (weakness or speech disturbance lasting ≥ 5minutes) or minor ischemic stroke (National Institute of Health Stroke Scale score ≤ 3) who underwent CT/CTA and subsequent MRI as part of the CATCH study. Results: 419 patients with TIA or Minor stroke were classified using TOAST, CCS, and ASCO. The proportion of patients with an undetermined etiology was 51.3% (215/419) with TOAST. This was significantly reduced by both CCS 36% (151/419, p< 0.001) and ASCO 41% (172/419, p< 0.001). CCS was also less likely to have an undetermined etiology as compared to ASCO (36% versus 41%, p = 0.024). When compared with TOAST, there was a 23.9% (95%CI:18.1- 29.7, P< 0.001) and 17.4% (10.1- 24.7, P< 0.001) reduction in the proportion of patients assigned to the undetermined group using CCS and ASCO respectively. The 8.5 % reduction in the undetermined group between CCS and ASCO was also statistically different P=0.031). Compared with ASCO1, CCS increased the assignment of patients to large artery disease (relative increase 7.4% {4.3-10.4}, P< 0.001) and Cardio-embolism/cardio-aortic categories (relative increase 8.1% {4.6-11.5}, P< 0.001). Conclusions: Both CCS and ASCO were superior to TOAST in assigning fewer patients to an undetermined etiology category. CCS was superior to ASCO at reducing the proportion of patients with undetermined etiology. This was largely driven by increased assignment in the large artery and Cardio-aorto embolic categories.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".