The exact science of stroke thrombolysis and the quiet art of patient selection
Bibliographic record
Abstract
The science of metric-based patient stratification for intravenous thrombolysis, revolutionized by the landmark National Institute of Neurological Disorders and Stroke trial, has transformed acute ischaemic stroke therapy. Recanalization of an occluded artery produces tissue reperfusion that unequivocally improves outcome and function in patients with acute ischaemic stroke. Recanalization can be achieved mainly through intravenous thrombolysis, but other methods such as intra-arterial thrombolysis or mechanical thrombectomy can also be employed. Strict guidelines preclude many patients from being treated by intravenous thrombolysis due to the associated risks. The quiet art of informed patient selection by careful assessment of patient baseline factors and brain imaging could increase the number of eligible patients receiving intravenous thrombolysis. Outside of the existing eligibility criteria, patients may fall into therapeutic 'grey areas' and should be evaluated on a case by case basis. Important factors to consider include time of onset, age, and baseline blood glucose, blood pressure, stroke severity (as measured by National Institutes of Health Stroke Scale) and computer tomography changes (as measured by Alberta Stroke Programme Early Computed Tomography Score). Patients with traditional contraindications such as wake-up stroke, malignancy or dementia may have the potential to receive benefit from intravenous thrombolysis if they have favourable predictors of outcome from both clinical and imaging criteria. A proportion of patients experience complications or do not respond to intravenous thrombolysis. In these patients, other endovascular therapies or a combination of both may be used to provide benefit. Although an evidence-based approach to intravenous thrombolysis for acute ischaemic stroke is pivotal, it is imperative to examine those who might benefit outside of protocol-driven practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".