The Symptom Experience of Patients With Atrial Fibrillation Before Their Initial Diagnosis
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF), the most common form of dysrhythmia, steadily increases in prevalence with age. If left untreated, AF significantly increases the risk of stroke, heart failure, and death. Despite the increasing prevalence, there are significant research gaps in the prediagnosis symptom experiences of patients with AF. OBJECTIVE: The purpose of this qualitative descriptive study was to explore the prediagnosis symptom experience of patients with AF. METHODS: Participants 19 years or older with AF diagnosed in the previous year were recruited (n = 26) from outpatient cardiac rehabilitation and AF clinics. Semistructured interviews, broadly guided by the Symptom Experience Model, explored perceptions, evaluations, and responses to AF symptoms. Thematic analysis used a 2-step approach, deductively coding for participants' symptom perceptions, evaluations, and responses and inductively coding within these broader Symptom Experience Model concepts. RESULTS: Perception involved awareness of bodily sensations, ranging from imperceptible noticing to commanding attention, heightened by rest and activity. In evaluation, participants used self-derived theories to explain their symptoms, gathered evidence to support/refute their theories, and formulated alternative theories as symptoms changed over time. Responses consisted of nontreatment, self-treatment, and health seeking; most participants needed repeated healthcare visits before diagnosis. CONCLUSIONS: The current study identified challenges participants experienced in developing awareness of their AF symptoms, the complex cognitive processes associated with evaluation, and barriers that made it difficult to respond to AF symptoms in a timely manner. Understanding the prediagnosis symptom experience from the patient's perspective is essential for the enhancement of current AF screening practices.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".