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Record W2896690691 · doi:10.1161/str.49.suppl_1.tp198

Abstract TP198: Risk of 30-day Hospital Readmission: Does Ischemic Stroke Subtype Matter?

2018· article· en· W2896690691 on OpenAlexaff
Anna Therese Bjerkreim, Andrej Netland Khanevski, Henriette Aurora Selvik, Lars Thomassen, Ulrike Waje‐Andreassen, Halvor Næss, Nicola Logallo

Bibliographic record

VenueStroke · 2018
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsAurora College
Fundersnot available
KeywordsMedicineStroke (engine)EtiologyInternal medicineSequelaProportional hazards modelIschemic strokeDiseaseMedical recordPediatricsEmergency medicineSurgeryIschemia

Abstract

fetched live from OpenAlex

Background: Stroke patients are at high risk of new diseases. However, as there are differences in risk factors, outcome and treatment for the various ischemic stroke (IS) and transient ischemic attack (TIA) etiologies, there may also be differences in their risk and causes of readmission. We aimed to investigate frequency and causes for 30-day readmission for the different IS subtypes, and estimate each subtypes risk of cause-specific 30-day readmission. Methods: All surviving IS or TIA patients admitted to a large Norwegian Hospital between July 2007 and January 2014 were followed by review of medical records. Main outcome of interest was the first unplanned readmission within 30 days after discharge. Stroke etiology was classified according to the TOAST criteria as large-artery atherosclerosis (LAA), cardioembolism (CE), small vessel occlusion (SVO), stroke of other demonstrated cause (SOC), or stroke of undetermined cause (SUC). Cox regression was performed to assess 30-day readmission risk of all-cause and cause-specific readmission for the different IS subtypes. Results: Of 1890 patients, 10.6 % were readmitted within 30 days (43/245 (17.6%) LAA, 75/614 (12.2%) CE, 12/205 (5.9%) SVO, 6/33 (18.2%) SOC, 65/793 (8.2%) SUC). Most frequent causes were stroke-related events (sequela, progressive stroke and neurological symptoms), infections, recurrent stroke and heart disease, but causes of readmission were unevenly distributed among the different stroke subtypes. Patients with LAA or SOC had significant higher risk of all-cause readmission and recurrent stroke, and patients with SUC had significant lower risk of all-cause readmission. Conclusion: We found significant variations in frequency and causes of 30-day readmission for the different IS subtypes. This approach supports the concept of IS as an polyetiologic disease, with unevenly distributed risk factors and comorbidity between the different etiologies. At the conference, we will also present and discuss predictors for 30-day all-cause readmission.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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.

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
Published2018
Admission routes1
Has abstractyes

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