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Record W4412412498 · doi:10.5853/jos.2025.00619

Acute Ischemic Stroke in the Incarcerated: Comparison of Treatment Rates and Clinical Outcomes With the United States General Population

2025· article· en· W4412412498 on OpenAlexaff
Alis J. Dicpinigaitis, Mill Etienne, Thanh N. Nguyen, A. Hassan, Priyank Khandelwal, Pankajavalli Ramakrishnan, Gábor Tóth, Mohammad El‐Ghanem, Krishna Amuluru, Viktor Szeder, J Lesley Crow, Karol P. Budohoski, Zurab Nadareishvilli, Kaustubh Limaye, Fazeel Siddiqui, Hamza Shaikh, Nishita Singh, Hesham Masoud, Taha Kass‐Hout, Sushanth Aroor, Shashvat M. Desai, Santiago Ortega-Gutierrez, Kaiz Asif, Dileep R. Yavagal, Fawaz Al‐Mufti

Bibliographic record

VenueJournal of Stroke · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineStroke (engine)Ischemic strokeEmergency medicinePopulationDemographyInternal medicineIschemiaEnvironmental health

Abstract

fetched live from OpenAlex

Incarcerated individuals in state and federal prisons demonstrate disproportionately high rates of cardiovascular disease, especially hypertension and tobacco use. 1 As a result, in comparison with the United States general population, inmates are faced with a more than three-fold risk of acute ischemic stroke (AIS). 2 Unfortunately, some evidence suggests that incarcerated individu-als receive suboptimal medical care due to structural barriers in healthcare delivery as well as discrimination based on incarcerated status, 3 which underscores a public health and human rights concern.To the best of our knowledge, no studies to date have evaluated treatment rates and clinical outcomes of AIS in this specialized population, in whom healthcare outcomes in general are woefully understudied.Herein, we aim to evaluate treatment rates of reperfusion therapies as well as clinical outcomes of in-

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.029
GPT teacher head0.368
Teacher spread0.339 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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