Abstract TP214: Progression of Stroke Deficits in Patients Presenting With Mild Symptoms: The Underlying Etiology Determines Prognosis
Bibliographic record
Abstract
Introduction: Many acute stroke patients present with mild symptoms, make it difficult to determine whether they should be treated with reperfusion strategies or not. The symptoms of such patients may frequently show progression. We hypothesized that localization based on clinical examination and multi-model imaging will be very helpful in determining patients most likely to have a bad outcome. Methods: We interrogated the Hamad Stroke Database to evaluate 90-days outcome in patients with acute ischemic stroke admitted within 4 hours and a NIHSS score of ≤6. Patients were evaluated based on the localization (lacunar or cortical), abnormalities on multi-model imaging and whether they were treated with rt-PA or not. The 90-day mRS was used to determine outcome. Results: During the study period 4016 patients were admitted with acute stroke. Mild stroke with arrival within 4 hours was diagnosed in 365 patients [no thrombolysis: 269 (lacunar: 155; cortical: 114), thrombolysis: 96 (lacunar: 38; cortical: 58)]. The rt-PA treated patients had significantly higher NIHSS (4.8±1.2 versus 2.3±1.6, p<0.0001), increased risk of complications (18.8% versus 4.1%, p<0.001) and longer hospital stay (median -Inter quartile range- 4 {2-6} versus 3 {2-5} days, p=0.002). Imaging abnormalities, including intracranial arterial occlusions and CTP mismatch were more frequent in rt-PA treated patients. The mRS at discharge (36.5% versus 14.8%, p<0.001) and 90 days (24% versus 12.6%, p=0.009) was significantly worse in patients with both cortical stroke in rt-PA-treated and untreated patients. Conclusions: The prognosis in patients with mild stroke depends on location of the lesion (lacunar versus cortical). Patients who receive rt-PA have significantly larger deficits, increased imaging abnormalities and higher rates of complications that may explain the poor prognosis in such subjects.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".