Abstract TMP89: Prior Antithrombotic Use is Associated With Favorable Mortality and Functional Outcomes in Acute Ischemic Stroke: Evidence From Get With the Guidelines-stroke
Bibliographic record
Abstract
Introduction: Trials have demonstrated that antithrombotic medications can prevent ischemic stroke, but it is unclear if prior therapy can also improve outcomes from those still having an acute ischemic stroke. Hypothesis: Antithombotics are the mainstay of treatment in primary and secondary prevention of stroke and thus their use prior to an acute event may be associated with better outcomes. Methods: We studied 540,993 patients with acute ischemic strokes between Oct 2011 and Mar 2014 from 1661 hospitals participating in Get With The Guidelines-Stroke. The associations between prior antithrombotic use and hospital discharge outcomes were examined, controlling for patient and hospital characteristics. Results: There were 250,104 (46%) stroke patients not receiving any antithrombotic prior to stroke; of whom 31.4% had a documented prior vascular indication. Patients taking antithrombotics prior to stroke were older and had more vascular risk factors. After adjustment for baseline prognostic factors, patients on chronic antithrombotics had better outcomes compared with those who did not (Table 1), regardless of whether a prior vascular indication was present or not. Similar results were observed in a subset of patients in whom NIHSS was available (n=415,034). Conclusions: Prior antithrombotic therapy was independently associated with improved clinical outcomes after acute ischemic stroke. Ensuring use of antithrombotics in appropriate patient populations may be associated with benefits beyond stroke prevention. Table 1
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".