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Record W4391018037 · doi:10.4103/sjmms.sjmms_62_23

Primordial and Primary Prevention of Ischemic Stroke in Saudi Arabia: A Combination Approach and Evolving Concepts

2024· article· en· W4391018037 on OpenAlexaff
Hussein Algahtani, Bader Shirah, Vladimir Hachinski

Bibliographic record

VenueSaudi Journal of Medicine and Medical Sciences · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineGovernment (linguistics)Stroke (engine)Primary preventionPrimary carePopulationDiseaseHealth carePrimary health careSecondary preventionHazardFamily medicineEnvironmental healthEconomic growthPathology

Abstract

fetched live from OpenAlex

Ischemic stroke is a considerable public health hazard and a significant cause of disability and mortality in Saudi Arabia. Primary prevention strategies in the country are currently limited. With the health sector transformation program that depends on the principles of value-based care and applying the new model of care in disease prevention, aggressive and serious steps for primary stroke prevention are expected to be implemented. This article reviews primordial and primary prevention of ischemic stroke in Saudi Arabia and suggests a combination approach and framework for implementation. We provide a pragmatic solution to implement primordial and primary stroke prevention in Saudi Arabia and specify the roles of the government, health professionals, policymakers, and the entire population. Currently, there are several key priorities for primordial and primary stroke prevention in Saudi Arabia that should target people at different levels of risk. These include an emphasis on a comprehensive approach that includes both individual and population-based strategies and establishing partnerships across health-care providers to share responsibility for developing and implementing both strategies. This is an urgent call for action to initiate different strategies suggested by experts for primary stroke prevention in Saudi Arabia.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.322
Teacher spread0.297 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2024
Admission routes1
Has abstractyes

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