The symptom experience of early and late treatment seekers before an atrial fibrillation diagnosis
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation is a complex condition associated with a broad spectrum of symptoms, coupled with variability in the frequency, duration and severity of symptoms. Early treatment seeking is important to reduce the risk of stroke, heart failure and dementia. Despite the increasing prevalence, there remains a limited understanding of the symptom experience prior to an atrial fibrillation diagnosis, and how these experiences influence treatment-related decisions and time frames. AIMS: This qualitative study aimed to explore the symptom experiences of patients receiving an early diagnosis of less than 48 hours and a late diagnosis of 48 hours or more after symptom awareness. METHODS: Twenty-six adults were interviewed guided by the symptom experience model. The symptom checklist was used to probe patient's symptoms further. Data were analysed using a two-step approach to thematic analysis utilising concepts from the symptom experience model. RESULTS: The two groups differed in their perception, evaluation and response to symptoms. The early diagnosis group (n = 6) experienced traumatic, severe and persistent symptoms, evoking concern and urgent treatment seeking. Conversely, the late diagnosis group (n = 20) reported more vague, paroxysmal symptoms that were readily ignored, self-theorised as non-illness related, and engaged in non-treatment strategies. Healthy self-perceptions, past experiences, atrial fibrillation knowledge and healthcare provider interactions influenced early or late treatment seeking. CONCLUSION: For many, the atrial fibrillation pre-diagnosis was a tumultuous period, requiring prolonged periods to recognise symptoms and formulate treatment-seeking responses. This study may promote future research and strategies aimed at facilitating the early identification and response to symptoms among atrial fibrillation patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".