National Tunisian Registry of Atrial Fibrillation (NATURE-AF): Protocol for the Design and Rationale (Preprint)
Bibliographic record
Abstract
BACKGROUND Atrial fibrillation (AF) is an important health problem in Tunisia. A significant change in the epidemiologic pattern of heart disease has been seen in the last 3 decades; however, no large prospective multicenter trial reflecting national data has been published so far. Robust data on the contemporary epidemiologic profile and management of atrial fibrillation (AF) patients in Tunisia are limited. OBJECTIVE The aim of this study was to analyze, follow, and evaluate patients with AF in a large multicenter nationwide trial. METHODS A total of 1800 consecutive patients with AF by electrocardiogram, reflecting all populations of all geographical regions of Tunisia, will be included in the study, with the objective to describe the epidemiologic pattern of AF. Patients will be officially enrolled in the NATURE-AF only if an electrocardiogram diagnosis (12-lead, 24-hour Holter, or other electrocardiographic documentation) confirming AF is made. The qualifying episode of AF should have occurred within the last year, and patients do not need to be in AF at the time of enrollment. Patients will be followed for 1 year. Incidence of stroke or transient ischemic attack, thromboembolic events, and cardiovascular death will be recorded as the primary end point and hemorrhagic accidents, measurement of international normalized ratio, and time in therapeutic range will be recorded as secondary end points. RESULTS Results w be available at the end of the study; the demographic profile and general risk profile of Tunisian AF patients, frequency of anticoagulation, frequency of effective treatment, and risks of thromboembolism and bleeding will be evaluated according to the current guidelines. Major adverse events will be determined. NATURE-AF will be the largest registry for North African AF patients. CONCLUSIONS This study would add data and provide a valuable opportunity for real-world clinical epidemiology in North African AF patients with insights into the uptake of contemporary AF management in this developing region. CLINICALTRIAL ClinicalTrials.gov NCT03085576; https://clinicaltrials.gov/ct2/show/NCT03085576 (Archived by WebCite at http://www.webcitation.org/6zN2DN2QX)
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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 | 0.049 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.128 | 0.031 |
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".