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Record W2979249635 · doi:10.1111/jgs.16178

Geriatric Elements and Oral Anticoagulant Prescribing in Older Atrial Fibrillation Patients: SAGE‐AF

2019· article· en· W2979249635 on OpenAlexaff
Jane S. Saczynski, Saket Sanghai, Catarina I. Kiefe, Darleen Lessard, Francesca Marino, Molly E. Waring, David C. Parish, Robert Helm, Felix Sogade, Robert J. Goldberg, Jerry H. Gurwitz, Weijia Wang, Tanya Mailhot, Benita A. Bamgbade, Bruce Barton, David D. McManus

Bibliographic record

VenueJournal of the American Geriatrics Society · 2019
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMontreal Heart Institute
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineAtrial fibrillationOral anticoagulantSAGEAnticoagulantInternal medicineCardiologyIntensive care medicineWarfarin

Abstract

fetched live from OpenAlex

OBJECTIVES: Oral anticoagulants are the cornerstone of stroke prevention in high-risk patients with atrial fibrillation (AF). Geriatric elements, such as cognitive impairment and frailty, commonly occur in these patients and are often cited as reasons for not prescribing oral anticoagulants. We sought to systematically assess geriatric impairments in patients with AF and determine whether they were associated with oral anticoagulant prescribing. DESIGN: Cross-sectional analysis of baseline data from the ongoing Systematic Assessment of Geriatric Elements in Atrial Fibrillation (SAGE-AF) prospective cohort study. SETTING: Multicenter study with site locations in Massachusetts and Georgia that recruited participants from cardiology, electrophysiology, and primary care clinics from 2016 to 2018. PARTICIPANTS: -VASc (congestive heart failure; hypertension; aged ≥75 y [doubled]; diabetes mellitus; prior stroke, transient ischemic attack, or thromboembolism [doubled]; vascular disease; age 65-74; female sex) score of 2 or higher, and no oral anticoagulant contraindications (n = 1244). MEASUREMENTS: A six-component geriatric assessment included validated measures of frailty, cognitive function, social support, depressive symptoms, vision, and hearing. Oral anticoagulant use was abstracted from the medical record. RESULTS: A total of 1244 participants (mean age = 76 y; 49% female; 85% white) were enrolled; 42% were cognitively impaired, 14% frail, 53% pre-frail, 12% socially isolated, and 29% had depressive symptoms. Oral anticoagulants were prescribed to 86% of the cohort. Oral anticoagulant prescribing did not vary according to any of the geriatric elements (adjusted odds ratios [ORs] for oral anticoagulant prescribing and cognitive impairment: OR = .75; 95% confidence interval [CI] = .51-1.09; frail OR = .69; 95% CI = .35-1.36; social isolation OR = .90; 95% CI = .52-1.54; depression OR = .79; 95% CI = .49-1.27; visual impairment OR = .98; 95% CI = .65-1.48; and hearing impairment OR = 1.05; 95% CI = .71-1.54). CONCLUSION: Geriatric impairments, particularly cognitive impairment and frailty, were common in our cohort, but treatment with oral anticoagulants did not differ by impairment status. These geriatric impairments are commonly cited as reasons for not prescribing oral anticoagulants, suggesting that prescribers may either be unaware or deliberately ignoring the presence of these factors in clinical settings. J Am Geriatr Soc 68:147-154, 2019.

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 score0.009
Threshold uncertainty score0.441

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 head0.020
GPT teacher head0.294
Teacher spread0.273 · 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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Citations67
Published2019
Admission routes1
Has abstractyes

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