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Record W2888916466 · doi:10.1093/eurheartj/ehy564.p980

P980Physical activity and outcome in patients with atrial fibrillation

2018· article· en· W2888916466 on OpenAlexaff
Roman Brenner, Stefanie Aeschbacher, Steffen Blum, Pascal Meyre, Peter Ammann, Paul Erné, Giorgio Moschovitis, Marcello Di Valentino, Dipen Shah, J. Schlaepfer, M Kuehne, Christian Sticherling, Stefan Osswald, David Conen

Bibliographic record

VenueEuropean Heart Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsPopulation Health Research Institute
Fundersnot available
KeywordsMedicineAtrial fibrillationCardiologyInternal medicineOutcome (game theory)

Abstract

fetched live from OpenAlex

Background: Increased physical activity (PA) is associated with an improved prognosis in healthy individuals and patients with cardiovascular diseases. However, the benefits of PA and the amount needed are much less clear among patients with atrial fibrillation (AF). Methods: The Basel Atrial Fibrillation Cohort study (BEAT-AF) is a prospective, multicenter cohort study that enrolled 1541 patients with documented AF. Patients reported whether they perform exercise on a regular basis. PA was quantified using the International Physical Activity Questionnaire (IPAQ). Three PA groups were defined: group 1: sedentary lifestyle; group 2: any moderate PA but no vigorous PA; group 3: any vigorous PA. The occurrence of all-cause death, cardiovascular death, major adverse cardiovascular events (MACE), major bleedings, heart failure hospitalization, stroke and myocardial infarction was assessed. To assess the relationships between PA and outcomes, Cox proportional hazards models were used to calculate hazard ratios (HR) and to adjust for clinically important confounders.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.340
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes1
Has abstractyes

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