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17-03: Effect of Age on Atrial Fibrillation Patient's Knowledge and Perceptions about Oral Anticoagulation Therapy: Results from an International Survey

2016· article· en· W2602426405 on OpenAlexaboutno aff
Deirdre A. Lane, Juliane Meyerhoff, Ute Rohner, Gregory Y.H. Lip

Bibliographic record

VenueEP Europace · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAtrial fibrillationStroke (engine)Internal medicinePhysical therapy

Abstract

fetched live from OpenAlex

Background: Stroke knowledge and perception of stroke risk are likely to affect oral anticoagulant (OAC) treatment preferences in patients with atrial fibrillation (AF). This international prospective study investigated the influence of age on patient perceptions of AF, stroke knowledge, preferences for oral anticoagulation (OAC) treatment decisions, and attributes of OAC affecting treatment choice. Methods: Cross-sectional survey of 937 AF patients receiving OAC [overall mean (SD) age 54.3(16.6) years; 37.1% female; mean (SD) CHA2 DS2 -VASc score 2.6(1.7)] recruited from 5 countries (USA, Canada, Germany, Japan, France) in those aged <65 (n = 628) and ≥65 years (n = 309). Results: Significantly fewer elderly patients had experienced a recent stroke and were less often concerned about stroke (Table). Good levels of stroke knowledge were higher in older AF patients (p < 0.001) and self-reported adherence to OAC was higher (p < 0.05). Stroke prevention was the most important factor when choosing OAC, particularly among older AF patients. Younger patients were more concerned by side effects other than major bleeding, dietary restrictions, and antidote availability when choosing OAC than older patients. *p < 0.05; **p < 0.001 vs. patients <65 years Conclusion: Younger AF patients were more concerned about stroke but had poorer stroke knowledge, and reported lower adherence to OAC. Stroke prevention is the most important factor when choosing OAC, regardless of age, with younger patients more concerned about side effects, antidote availability and dietary restrictions.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.175
Threshold uncertainty score0.383

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.120
GPT teacher head0.477
Teacher spread0.356 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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