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Record W4406962928 · doi:10.1161/str.56.suppl_1.tp148

Abstract TP148: The Need for Standardized Stroke Care: Assessing Global Provider Training and Institutional Disparities

2025· article· en· W4406962928 on OpenAlexaff
Cameron D. Owens, Camila Bonin Pinto, Mirjam R. Heldner, Leonardo Augusto Carbonera, Ana Paula de Souza, Christine Tunkl, Sarah Shali Matuja, Karen Orjuela, Rodrigo Guerrero, Matías Alet, Stephanie Vasquez, Vanessa Cano-Nigenda, Luis Roa Wandurraga, Javier Lagos-Servellón, Gustavo Saposnik, Julieta Rosales, Faddi G. Saleh Velez

Bibliographic record

VenueStroke · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineStroke (engine)Family medicineGerontology

Abstract

fetched live from OpenAlex

The development of vascular neurology as the primary specialty for stroke evaluation and treatment is a relatively recent advancement. Vascular neurology is well-established in the United States, ensuring most stroke patients receive specialized care, but many stroke patients globally are managed by physicians without specialized training in cerebrovascular and neurological medicine that is partly manifested by the availability of institutions that incorporate dedicated stroke training. Our goal is to evaluate the training of stroke providers worldwide to identify who is leading stroke care in different regions, and to assess the availability of dedicated stroke education within these regions. This evaluation aims to highlight disparities and ultimately work towards the global standardization of stroke care. We distributed an international survey to assess level of training of physicians treating stroke, if dedicated stroke training (e.g., fellowships) is available at their institution and location where they practice. Preliminary results from our ongoing survey show the average age of 83 respondents was 38.29 ± 7.72 and no difference in proportion of males (n=44) and females (n=39, p=0.0025). Physicians who treat the hyperacute phase of stroke indicated their level of training (Vascular Neurologist n=19, Other n=40, p=0.0045) ( Figure 1 ). A subset analysis of non-vascular neurologists revealed that most ‘Other’ physicians worked in areas where no vascular neurology training was available (vascular neurology training available n=11, not available n=24, p=0.0410) ( Figure 2 ). Distribution of vascular neurologist’s vs other physicians treating stroke indicate countries where there is increased proportion (i.e UK, Switzerland, Czech Republic, Argentina, USA) and decreased proportion of dedicated vascular neurology training (Honduras, Colombia, Nepal, Kenya, Papua New Guinea, Tanzania, Bolivia, Hungary) ( Figure 3 ). While the availability of vascular neurology is growing internationally, our preliminary results from this ongoing survey indicate there is still a disparity amongst currently practicing physicians who are taking care of acute and subacute patients. Accordingly, this disparity in training may affect patient outcomes; hence, these preliminary data serve to bolster stroke care internationally by identifying areas of training and institutional disparity, all with the goal of improving outcomes in stroke patients through global standardization of care.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.992
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.028
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.112
GPT teacher head0.483
Teacher spread0.371 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
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

Citations0
Published2025
Admission routes1
Has abstractyes

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