Characterizing In-Hospital Acute Ischemic Strokes: Clinical Profiles and Predictors of Acute Treatment
Bibliographic record
Abstract
INTRODUCTION: Treatment of in-hospital acute ischemic stroke (AIS) is challenging. We aimed to characterize in-hospital AIS and identify predictors of receiving thrombolysis and thrombectomy. METHODS: We conducted an analysis of a retrospective cohort study using administrative data and chart review as part of the INPATIENTS study (Comparing In-Hospital and Community-Onset Strokes in Alberta). All in-hospital AIS patients admitted in the province between January 1, 2018, and December 31, 2022, were included. Clinical characteristics and quality of care measures were compared between treated and non-treated patients. We used multivariable logistic regression to identify predictors of acute treatment and assessed model performance using ROC curves and calibration plots. RESULTS: Only 7.3% (158 of 2,159) in-hospital AIS patients received thrombolysis or thrombectomy between 2018 and 2022. Treated patients had higher NIHSS scores (median 12 vs. 8), fewer recent invasive procedures (42% vs. 53%), and were less likely to have altered consciousness (12.0% vs. 52.1%). Common reasons for not receiving thrombolysis included delayed recognition and recent procedures. Treated patients more often received standard stroke evaluation. The final logistic regression model included age, sex, NIHSS, altered consciousness, admitting service, and comorbidities as predictors of treatment. It showed good discrimination (AUC = 0.8371), though calibration issues may affect its generalizability. CONCLUSION: In-hospital AIS patients treated with thrombolysis and thrombectomy had more severe strokes, were less likely to have altered consciousness, and more often received standard stroke evaluations than non-treated patients. These differences emphasize the need to better understand barriers and develop new approaches to in-hospital stroke management.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".