NursE led Atrial Fibrillation Management: The NEAT Study
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is a growing epidemic. Current models of care delivery are inadequate in meeting the needs of the population with AF. Furthermore, quality of life is known to be poor in patients with AF and is associated with adverse patient outcomes. OBJECTIVE: The aim of this study was to determine if nurse-led education and cardiovascular risk factor modification, undertaken using the principles of motivational interviewing, facilitated by an electronic decision support tool to ensure the appropriate use of oral anticoagulation (OAC), could improve health-related quality of life (HRQoL), guideline adherence to OAC, and cardiovascular risk factor profiles in individuals with AF. METHODS: This was a multicenter, prospective, randomized controlled feasibility study of 72 individuals with AF. The intervention involved 1 face-to-face nurse-delivered education and risk factor management session with 4 follow-up telephone calls over a 3-month period to monitor progress. The primary outcome measure was HRQoL as assessed by the Short Form-12 survey. RESULTS: A total of 72 participants were randomized, with 36 individuals in each arm completing follow-up. Mean age was 65 ± 11 years and 44% were women. At 3 months follow-up, no significant differences between groups were observed for the physical or mental component summary scores of the Short Form-12, nor any of the subscales. Appropriate use of OAC did not differ between groups at final follow-up. CONCLUSIONS: A brief nurse-delivered educational intervention did not significantly impact on HRQoL or risk factor status in individuals with AF. Further research should focus on interventions of greater intensity to improve outcomes in this population. TRIAL REGISTRATION: ACTRN12615000928516.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".