ABCD2, ABCD2-I, and OTTAWA scores for stroke risk assessment: a direct retrospective comparison
Bibliographic record
Abstract
Abstract Transient ischemic attack (TIA) is a neurologic emergency characterized by cerebral ischemia eliciting a temporary focal neurological deficit. Many clinical prediction scores have been proposed to assess the risk of stroke after TIA; however, studies on their clinical validity and comparisons among them are scarce. The objective is to compare the accuracy of ABCD2, ABCD2-I, and OTTAWA scores in the prediction of a stroke at 7, 90 days, and 1 year in patients presenting with TIA. Single-centre, retrospective study including patients with TIA admitted to the Emergency Department of our third-level, University Hospital, between 2018 and 2019. Five hundred three patients were included. Thirty-nine (7.7%) had a stroke within 1 year from the TIA: 9 (1.7%) and 24 (4.7%) within 7 and 90 days, respectively. ABCD2, ABCD2-I, and OTTAWA scores were significantly higher in patients who developed a stroke. AUROCs ranged from 0.66 to 0.75, without statistically significant differences at each time-point. Considering the best cut-off of each score, only ABCD2 > 3 showed a sensitivity of 100% only in the prediction of stroke within 7 days. Among clinical items of each score, duration of symptoms, previous TIA, hemiparesis, speech disturbance, gait disturbance, previous cerebral ischemic lesions, and known carotid artery disease were independent predictors of stroke. Clinical scores have moderate prognostic accuracy for stroke after TIA. Considering the independent predictors for stroke, our study indicates the need to continue research and prompts the development of new tools on predictive scores for TIA.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".