Abstract TP198: Risk of 30-day Hospital Readmission: Does Ischemic Stroke Subtype Matter?
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".