The prevalence, determinants, natural history and impact of atrial fibrillation and atrial flutter in patients with tuberculosis pericarditis - insights from the IMPI trial
Bibliographic record
Abstract
Tuberculosis is the most common cause of pericarditis in Africa. The dual human immunodeficiency virus (HIV)-tuberculosis epidemics are major contributors to the burden of extra-pulmonary tuberculosis, including tuberculous pericarditis. Mortality rates remain unacceptably high. Atrial fibrillation (AF) is the most common sustained arrhythmia encountered in clinical practice. It is associated with increased cardiovascular mortality and morbidity, as well as complications related to thromboembolic disease and haemodynamic instability. Similarly, atrial flutter (AFL) is a common macro-reentry arrhythmia, often associated with AF and its complications. While there is a recognized association between atrial fibrillation and / or atrial flutter (AF/AFL) and tuberculous pericarditis, there are limited data regarding the prevalence, determinants, natural history, and outcomes of AF/AFL in tuberculous pericarditis. Hypothesis: In patients with tuberculous pericarditis, AF/AFL is common, and when compared to tuberculous pericarditis patients that are in sinus rhythm, is associated with increased morbidity and mortality. Aims In participants with tuberculous pericarditis enrolled into the Investigation of the Management of Pericarditis (IMPI) trial, we intend to: 1. Estimate the prevalence of AF/AFL 2. Describe the natural history of AF/AFL 3. Identify clinical, biochemical and, echocardiographic predictors of AF/AFL 4. Determine the clinical impact of AF/AFL.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".