Abstract W P280: Predictors of Hospitalization in Patients with Minor Stroke and TIA
Bibliographic record
Abstract
Background: Although current guidelines recommend urgent treatment for patients with TIA and minor stroke, it is unclear if hospitalization is required, and little is known about the factors that influence decisions to admit such patients. The objective of this study was to identify patient and system-level predictors of admission in patients with minor stroke and TIA. Methods: The Ontario Stroke Registry (OSR) was used to identify a cohort of patients with acute minor stroke or TIA who presented to any one of Ontario’s 150 acute care hospitals between April 1, 2008 and March 31, 2011. Multivariable analysis using hierarchical logistic regression modeling was performed to identify predictors of hospitalization, including patient characteristics, risk factors, presenting symptoms, features of the care encounter and hospital and system-level factors. Results: The final study cohort consisted of 10,890 patients with minor stroke or TIA. Overall, 48% were women and the median age was 75 years. The overall admission rate was 57% (31% for TIA and 83% for minor stroke). In the multivariable analysis, significant predictors of hospital admission included disability prior to admission, risk factors including hyperlipidemia, atrial fibrillation and smoking, presenting with weakness or symptoms persisting for more than 60 minutes and arriving to hospital by ambulance (all p<0.01). Approximately 88% of the model’s variance was attributable to patient-level factors. Stroke center designation, hospital volume, having a stroke unit on site and having access to a stroke prevention clinic were not significant predictors of hospitalization. Similar results were found when the minor stroke and TIA subgroups were analyzed separately. Conclusions: These results suggest that patient-level characteristics rather than system-level factors have a greater influence on the decision to admit patients with minor stroke or TIA. Given that these patients are hospitalized solely for rapid completion of investigations and initiation of secondary prevention therapies, a system change providing access to specialized urgent TIA clinics is required to avoid the unnecessary costs of hospitalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".