Outpatient Practice Patterns After Stroke Hospitalization Among Neurologists
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Care after stroke hospitalization can provide several opportunities to optimize vascular risk reduction. However, not much is known about poststroke practice patterns among neurologists. Such knowledge may help direct specific efforts to improve the impact of practicing neurologists on clinical outcomes after stroke. METHODS: A survey soliciting information on processes of care in the outpatient setting after recent hospitalization for ischemic stroke or transient ischemic attack was mailed to a random sample of 833 US and Canadian neurologist-members of the American Academy of Neurology. RESULTS: A total of 475 (57%) responses were received. Practice demographics of survey responders and nonresponders were largely similar. Fourteen percent of respondents identified themselves as vascular neurologists. Overall, respondents reported frequently checking for medication adherence and counseling patients on lifestyle modification. However, neurologists reported screening more frequently for diabetes, hypertension, and dyslipidemia than actually treating these conditions (all P<0.0001) Vascular neurologists were more likely than general neurologists to screen for hypertension (97% versus 86%, P=0.016), dyslipidemia (94% versus 68%, P<0.001), diabetes (89% versus 62%, P<0.001), and sleep apnea (94% versus 79%, P=0.007) as well as to treat hypertension (71% versus 45%, P<0.001), dyslipidemia (82% versus 50%, P<0.001), diabetes (45% versus 21%, P<0.001), and current smoking (77% versus 59%, P=0.005). Neurologists with mostly government-insured and uninsured patients were significantly more likely to engage in vascular risk reduction treatment than neurologists with mostly commercially insured patients. CONCLUSIONS: Self-reported rates of screening and treatment of major vascular risk factors by most neurologists after stroke hospitalization are substantial but not universal. Bridging knowledge gaps or adopting a systematic management approach in coordination with primary care physicians could help optimize poststroke care.
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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 | 0.000 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".