Clinical features, risk factors, and short-term outcome of ischemic stroke, in patients with atrial fibrillation: Data from a population-based study
Bibliographic record
Abstract
Objectives: Atrial fibrillation (AF) is the most common sustained cardiac rhythm disorder associated with stroke. This study was done to describe risk factors, clinical features, and short-term outcomes of stroke patients with AF. Materials and Methods: This study was a part of the Indian Council of Medical Research funded “Ludhiana urban population based Stroke Registry.” Data were collected using WHO STEPS stroke method. All patients ≥18 years of age, who developed ischemic stroke between March 26, 2011, and March 25, 2013, were included in this study. Data about demographic details, clinical features, and risk factors were collected. The outcome was assessed at 28 days using modified Rankin scale (mRs) (good outcome: mRS ≤2; poor outcome >2). The statistical measures calculated were descriptive statistics, Chi-square test, Fischer's exact test, and independent t-test. Results: Of the total 7199 patients enrolled in the registry, data of 1942 patients who fulfilled inclusion criteria were analyzed, and AF was seen in 203 (10%) patients. AF patients were older (AF 62 ± 14 vs. non-AF 60 ± 15 years, P = 0.01), had more hypertension (AF 176 [87%] vs. non-AF 1396 [80%], P = 0.03), hyperlipidemia (AF 60 [32%] vs. non-AF 345 [21%], P = 0.001), coronary artery disease (AF 60 [30%] vs. non-AF 195 [11%], P < 0.0001), and carotid stenosis (AF 14 [7%] vs. non-AF 57 (3%), P = 0.02). They had worse outcome (mRS >2; AF 90 [50%] vs. non-AF 555 [37%], P = 0.001). Conclusions: Ten percent of stroke patients had AF. They were older, had multiple risk factors and worse outcome. There was no gender difference in this large cohort.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".