Frequency and Determinants of Lipid Testing in Ischemic Stroke and Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: National guidelines recommend lipid testing for all patients with ischemic stroke and transient ischemic attack. This study examined the frequency and predictors for in-hospital low-density lipoprotein testing using data from a nationwide stroke registry. METHODS: Between 2003 and 2008, Get With The Guideline-Stroke (GWTG-Stroke) hospitals (n=981) contributed 479 284 consecutive ischemic stroke or transient ischemic attack admissions. Logistic regression models were used to determine patient and hospital characteristics associated with lipid testing. RESULTS: Frequency of LDL measurement increased from 54.3% in 2003 to 81.9% in 2008 (P<0.001), the adjusted OR for LDL measurement was 1.23 per additional calendar year (95% CI, 1.18 to 1.29; P<0.001). The frequency of LDL measurement also increased with longer hospital program participation; the adjusted OR was 1.17 per additional year of GWTG-Stroke participation (95% CI, 1.12 to 1.23; P<0.001). LDL measurement was lower in women, nonsmokers, those with atrial fibrillation, those with a history of stroke or transient ischemic attack, and in those with transient ischemic attack (versus ischemic stroke; all P<0.001). LDL >or=100 mg/dL was seen in 52.1% of those tested, including in 35.5% of patients already prescribed lipid-lowering therapy before admission. CONCLUSIONS: Rates of LDL measurement in hospitalized patients with ischemic stroke and transient ischemic attack have improved dramatically in this large quality improvement program, although disparities in testing still exist. Testing frequently revealed an LDL level that could prompt a change in clinical management.
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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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".