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Record W2535326255 · doi:10.1159/000449288

How Reliable Are Administrative Data for Capturing Stroke Patients and Their Care

2016· article· en· W2535326255 on OpenAlexafffundabout
Joan Porter, Luke Mondor, Moira K. Kapral, Jiming Fang, Ruth Hall

Bibliographic record

VenueCerebrovascular Diseases Extra · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsUniversity of TorontoOntario Stroke NetworkUniversity Health NetworkInstitute for Work & HealthInstitute for Clinical Evaluative Sciences
FundersOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative SciencesOntario Stroke NetworkHeart and Stroke Foundation of Canada
KeywordsMedicineStroke (engine)Atrial fibrillationEmergency medicineAuditIntracerebral hemorrhageMedical recordKappaPopulationInternal medicineMedical emergencySubarachnoid hemorrhage

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: The reliability of diagnostic coding of acute stroke and transient ischemic attack (TIA) in administrative data is uncertain. The purpose of this study is to determine the agreement between administrative data sources and chart audit for the identification of stroke type, stroke risk factors, and the use of hospital-based diagnostic procedures in patients with stroke or TIA. METHODS: Medical charts for a population-based sample of patients (n = 14,508) with ischemic stroke, intracerebral hemorrhage (ICH), or TIA discharged from inpatient and emergency departments (ED) in Ontario, Canada, between April 1, 2012 and March 31, 2013, were audited by trained abstractors. Audited data were linked and compared with hospital administrative data and physician billing data. The positive predictive value (PPV) of hospital administrative data and kappa agreement for the reporting of stroke type were calculated. Kappa agreement was also determined for stroke risk factors and for select stroke-related procedures. RESULTS: The PPV for stroke type in inpatient administrative data ranged from 89.5% (95% CI 88.0-91.0) for TIA, 91.9% (95% CI 90.2-93.5) for ICH, and 97.3% (95% CI 96.9-97.7) for ischemic stroke. For ED administrative data, PPV varied from 78.8% (95% CI 76.3-81.2) for ischemic, 86.3% (95% CI 76.8-95.7) for ICH, and 95.3% (95% CI 94.6-96.0) for TIA. The chance-corrected agreement between the audited and administrative data was good for atrial fibrillation (k = 0.60) and very good for diabetes (k = 0.86). Hospital administrative data combined with physician billing data more than doubled the observed agreement for carotid imaging (k = 0.65) and echocardiography (k = 0.66) compared to hospital administrative data alone. CONCLUSIONS: Inpatient and ED administrative data were found to be reliable in the reporting of the International Classification of Diagnosis, 10th revision, Canada (ICD-10-CA)-coded ischemic stroke, ICH and TIA, and for the recording of atrial fibrillation and diabetes. The combination of physician billing data with hospital administrative data greatly improved the capture of some diagnostic services provided to inpatients.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.713

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 communication0.0000.000
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 head0.030
GPT teacher head0.256
Teacher spread0.226 · 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

Citations82
Published2016
Admission routes3
Has abstractyes

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