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Record W2092154231 · doi:10.1161/strokeaha.110.592022

What Is the Future of Stroke Prevention?

2010· review· en· W2092154231 on OpenAlexaff
Walter N. Kernan, Lenore J. Launer, Larry B. Goldstein

Bibliographic record

VenueStroke · 2010
Typereview
Languageen
FieldMedicine
TopicMedication Adherence and Compliance
Canadian institutionsInstitute of Aging
FundersNational Institutes of Health
KeywordsPolypillMedicineStroke (engine)Medical prescriptionIntensive care medicinePopulationRandomized controlled trialPharmacologyAlternative medicineSurgeryEnvironmental healthPathology

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: The control of stroke risk factors remains challenging. The "polypill" concept represents a novel approach for reducing stroke and cardiovascular risk factors in the entire population. The polypill would include several components and be provided without prescription to all adults of a certain age. RESULTS: A polypill aimed at lowering blood pressure and cholesterol levels is estimated to potentially reduce the risk of a first ischemic stroke by 53%; this would translate to about 400 000 fewer strokes each year in the United States alone. Recommending a polypill for the entire older adult population would, however, include many individuals without the multiple risk factors targeted by its components, putting them at risk for drug-related side effects and responsible for the costs of a medication from which they would not derive benefit. Additional arguments for and against the polypill approach are discussed. CONCLUSIONS: Only clinical trials can provide the evidence needed to determine the usefulness of the polypill approach. Issues related to defining the components of the polypill, evaluating the pharmacodynamics and pharmacokinetics of a multiple-component formulation, and establishing safety and cost-effectiveness when given to large populations, however, are not trivial.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.052
GPT teacher head0.382
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations22
Published2010
Admission routes1
Has abstractyes

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