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Record W4390544964 · doi:10.1093/eurjcn/zvad139

More positive patient-reported outcomes in patients newly diagnosed with atrial fibrillation: a comparative longitudinal study

2024· article· en· W4390544964 on OpenAlexfundno aff
Lena Holmlund, Carl Hörnsten, Åsa Hörnsten, Karin Olsson, Fredrik Valham, Karin Hellström Ängerud

Bibliographic record

VenueEuropean Journal of Cardiovascular Nursing · 2024
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
FundersMedicinska fakulteten, Umeå UniversitetUmeå UniversitetHjärt-LungfondenRiksförbundet HjärtLungKempe FoundationFaculty of Medicine, University of British Columbia
KeywordsMedicineAtrial fibrillationInternal medicineCardiologyIntensive care medicine

Abstract

fetched live from OpenAlex

AIMS: To compare patient-reported outcomes (PROs) in patients newly (<6 months) diagnosed with atrial fibrillation (AF) with those who have had a longer diagnosis (≥6 months) and to investigate whether or not these outcomes change over a 6-month period. METHODS AND RESULTS: In this longitudinal survey study, 129 patients with AF completed the Revised Illness Perception Questionnaire, the Arrhythmia-Specific questionnaire in Tachycardia and Arrhythmia, and the Hospital Anxiety and Depression Scale at baseline and after 6 months. At baseline, patients newly diagnosed with AF (n = 53), compared with patients with a previous diagnosis (n = 76), reported AF as more temporary (P = 0.003) and had a higher belief in personal and treatment control (P = 0.004 and P = 0.041, respectively). At a 6-month follow-up, patients newly diagnosed reported a lower symptom burden (P = 0.004), better health-related quality of life (HRQoL); (P = 0.015), and a higher personal control (P < 0.001) than patients previously diagnosed. Over time, in patients newly diagnosed, symptom burden and the anxiety symptom score decreased (P = 0.001 and P = 0.014, respectively) and HRQoL improved (P = 0.002). CONCLUSION: Patients newly diagnosed with AF reported more positive PROs both at baseline and at a 6-month follow-up than patients with a previous diagnosis of AF. Therefore, it is important to quickly capture patients newly diagnosed to support their belief in their own abilities. Such support may, alongside medical treatments, help patients manage the disease, which may lead to reduced symptom burden and better HRQoL over time.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.325
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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