How Reliable Are Administrative Data for Capturing Stroke Patients and Their Care
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.107 | 0.411 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".