Patterns and Predictors of Discharge Statin Prescription Among Hospitalized Patients With Intracerebral Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Many patients hospitalized with intracerebral hemorrhage are at high future risk for ischemic events and may benefit from stain therapy. However, little is known about patterns of statin prescription among patients with intracerebral hemorrhage, especially after the finding of higher hemorrhagic stroke risk in the statin treatment arm of the Stroke Prevention by Aggressive Reduction in Cholesterol Levels (SPARCL) trial. We evaluated recent nationwide trends in discharge statin treatment after intracerebral hemorrhage hospitalization. METHODS: Using data from 25 673 patients with hemorrhagic stroke admitted to Get With Guidelines-Stroke participating hospitals between January 1, 2005, and December 31, 2007, we assessed factors associated with discharge statin prescription, including treatment over time and in relation to dissemination of the SPARCL results. Piecewise logistic multivariable regression models were fit to track statin use in various periods. RESULTS: Mean age was 67.9±15 years, 48.1% female, and discharge statin treatment in 39.5%. Variables independently associated with lower discharge statin use included female sex (OR 0.87, 95% CI, 0.82 to 0.93), prior stroke/transient ischemic attack (OR 0.85, 95% CI, 0.78 to 0.92), academic center (OR 0.87, 95% CI, 0.82 to 0.93), and Midwest region (OR 0.65, 95% CI, 0.56 to 0.80). Statin prescription climbed over the study period from 66.9% to 74.5% (P<0.001) among eligible patients with a decrease during SPARCL reporting (P=0.03) and then a return to prior levels thereafter. CONCLUSIONS: Discharge statin prescription among hospitalized patients with intracerebral hemorrhage has modestly risen over time. The clinical implications of this care pattern among patients with intracerebral hemorrhage require further study.
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 | 0.005 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".