Potential Clinical Correlates and Risk Factors for Interatrial Block
Bibliographic record
Abstract
BACKGROUND: Interatrial block (IAB; P wave > or =110 ms) denotes a conduction delay between the atria, is strongly associated with atrial tachyarrhythmias, left atrial enlargement, left atrial electromechanical dysfunction, and is a risk for embolism. Despite this, potential risk factors for IAB have not been clearly defined. METHODS: Patients admitted via the Emergency Department for nonacute medical reasons to the nontelemetry general medical floors of a tertiary care general hospital from October to November 2004 were screened for sinus rhythm on electrocardiograms. Four hundred and four patients who met our criteria were then evaluated for IAB on respective electrocardiograms. All patients were subsequently compared for common diseases as well as coronary artery disease (CAD) risk factors and divided into two groups, those with IAB and those without (control). Mean age +/- standard deviation, odds ratios (ORs), 95% confidence intervals (CIs), r values, and p values were calculated. p values <0.05 were considered statistically significant. RESULTS: From the sample (n = 404), 182 patients had IAB (45%; mean age 64.32 +/- 19.27 years; males 51.6%) while 222 did not (control). CAD (OR 3.150, 95% CI 2.05-4.83; p < 0.001, r = 0.3), hypertension (OR 2.918, 95% CI 1.85-4.60; p < 0.001, r = 0.2), diabetes mellitus (OR 2.542, 95% CI 1.62-3.97; p < 0.001, r = 0.1), and hypercholesterolemia (OR 1.823, 95% CI 1.22-2.74; p = 0.004, r = 0.2) were significant risk factors and correlates for IAB. Multivariate analysis using stepwise linear regression revealed these factors as direct correlates of IAB. CONCLUSION: CAD, hypertension, diabetes mellitus and hypercholesterolemia appear to be risk factors for IAB in general hospital patients admitted for nonacute reasons. Considering the known sequelae of IAB, awareness of its associations with such risk factors could be important for patient risk stratification.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".