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Record W4205453400 · doi:10.1177/17474930211066416

Stroke services in Africa: What is there and what is needed

2022· article· en· W4205453400 on OpenAlexaff
Tamer Roushdy, Hany Aref, Selma Kesraoui, Michael Temgoua, Kiatoko Ponte Nono, Meron Awraris Gebrewold, Peter Waweru, Urvashy Gopaul, Faouzi Belahsen, Djibrilla Ben‐Adji, Rita Melifonwu, Sanjeev Pugazhendhi, Noëmie Woodcock, Muhyadin Hassan Mohamed, Anastasia Rossouw, Sarah Shali Matuja, Mark Koba Ruanda, Chokri Mhiri, Deanna Saylor, Nevine El Nahas, Hossam Shokri

Bibliographic record

VenueInternational Journal of Stroke · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity Health Network
Fundersnot available
KeywordsMedicineStroke (engine)Incidence (geometry)Acute strokeHealth servicesMedical emergencyEnvironmental healthPopulationNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Over the past few years, the incidence and prevalence of stroke has been rising in most African countries and has been reported as one of the leading causes of morbidity and mortality. To study this problem, we need to realize the quality and availability of stroke care services as a priori to improve them. METHODS AND RESULTS: In this study, we investigated the availability of different stroke-related services in 17 countries from different African regions. An online survey was conducted and fulfilled by stroke specialists and included primary prevention, acute management, diagnostic tools, medications, postdischarge services, and stroke registries. The results showed that although medications for secondary prevention are available, yet many other services are lacking in various countries. CONCLUSION: This study displays the deficient aspects of stroke services in African countries as a preliminary step toward active corrective procedures for the improvement of stroke-related health services.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.267
Teacher spread0.253 · 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.

Study designNot applicable
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

Citations50
Published2022
Admission routes1
Has abstractyes

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