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Record W2235145925 · doi:10.1161/str.43.suppl_1.a3954

Abstract 3954: Does the Volume of Stroke/TIA Admissions Relate to Clinical Outcomes in the Ontario Stroke System?

2012· article· en· W2235145925 on OpenAlexaffabout
Ruth Hall, Jiming Fang, Kathryn Hodwitz, Mark Bayley

Bibliographic record

VenueStroke · 2012
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsToronto Rehabilitation InstituteInstitute for Clinical Evaluative Sciences
Fundersnot available
KeywordsMedicineStroke (engine)Diabetes mellitusEmergency medicine

Abstract

fetched live from OpenAlex

Background: Previous research has found decreased mortality rates among hospitals that treat high volumes of patients for specific surgical and medical conditions. The degree of association between mortality and volume varies substantially by condition and procedure, and while this relationship has been examined for many surgical procedures and medical conditions, few studies have looked at stroke We examine the volume-outcome relationship among Ischemic stroke patients. Knowledge of how to guide access to care for people with stroke is of increasingly concern given expected growing costs of medical care. Methods: All ischemic stroke separations at 128 acute hospitals in the province of Ontario from 2003 to 2009 were analyzed using administrative databases. Spline plots were used to establish small, medium and high volume-based categories. Mulivariate hierarchical modeling was used to evaluate the volume outcome relationship. The outcome of interest was 30-day mortality. Results: From 2003 through 2009, 71,856 hospitalizations for stroke/TIA occurred in 128 hospitals. The mean (+/- SD) number of annual hospitalizations for stroke/TIA was 43(30) for small volume hospitals, 157(55) for medium volume hospitals and 278(73) for high volume hospitals. Patient Characteristics: Patients admitted to high volume hospitals were younger, 54% of patients are 75 years of age or older compared to 61% at small volume hospitals (p <0.0001). Patients at small volume hospitals were similar with respect to prevalence of comordid conditions with exception of hypertension, cancer, diabetes, old AMI and renal disease where patients at small volume hospitals had lower prevalence (p < 0.01). Hospital Characteristics: Approximately 15% were specialized stroke centres and 7% had the capacity to provide neurosurgical services. Large volume hospitals were more likely to be specialized stroke centres and or teaching hospitals compared to medium or small hospitals. Overall 30-day risk-adjusted ischemic stroke mortality over the seven year period was 17%. Patients cared for in low volume hospitals have a 30% higher mortality rate compared to patients cared for in a high volume hospital. There was no statistically significant difference in 30-day mortality between medium and high volume hospitals. Conclusions: Similar to procedure-based volume outcome relationships, there does appear to be an association between acute hospital stroke volume and 30-day mortality among ischemic stroke patients in Ontario. Hospitals that have average annual stroke volumes greater than 15 but less than 45 per year have 30 day mortality rates 30% higher than hospitals that see on average 278 stroke/TIA patients per year.itals. The critical threshold is about 150 patients per year. This could be explained by higher likelihood of admission to stroke units or to the expertise developed by regularly caring for stroke patients.

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)0.007
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 score0.989
Threshold uncertainty score0.210

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.323
Teacher spread0.292 · 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".

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Citations0
Published2012
Admission routes2
Has abstractyes

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