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Record W3004043939 · doi:10.14740/cr972

Paroxysmal Atrial Fibrillation: Insight Into the Intimate Mechanisms of Coagulation

2020· article· en· W3004043939 on OpenAlexvenueno aff
Мariya Negreva, Zarkova Ana, Krasimira Prodanova, Petar Petrov

Bibliographic record

VenueCardiology Research · 2020
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineAtrial fibrillationCardiologyCoagulationFactor VIIGastroenterologyEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Atrial fibrillation (AF) is a hypercoagulable state. However, the intimate mechanisms leading to impaired coagulation and the timing of their activation are unclear. The aim of the study was to investigate the factors that initiate the coagulation cascade in the early hours (up to 48 h) of clinical manifestation of paroxysmal atrial fibrillation (PAF). METHODS: Tissue factor (TF) level, coagulation activity of factor VII (FVIIa), factor XII (FXIIa) and factor XI (FXIa) were measured in plasma of 51 non-anticoagulated patients (26 men and 25 women, aged 59.84 ± 11.42 years) and 52 controls (26 men and 26 women, aged 59.50 ± 10.53 years) by enzyme-linked immunoassays and kinetic assays. RESULTS: TF was higher in the PAF group (268.63 ± 90.62 pg/mL vs. 170.21 ± 66.19 pg/mL, P < 0.001) as well as FVIIa (170.82±59.39% vs. 95.17±37.90%, P < 0.001), FXIIa (218.31±84.04% vs. 148.41±53.94%, P < 0.001) and FXIa (178.41±55.94% vs. 111.75±37.33%, P < 0.001). Regression analysis showed that in the first 6 h of the disease, increase in time led to increase in FXIIa (r = 0.25, P < 0.05), FXIa (r = 0.75, P < 0.05), TF level (r = 0.25, P < 0.05) and FVIIa (r = 0.25, P < 0.05). CONCLUSION: Hemocoagulation changes were observed even < 6 h after the onset of the disease. They suggest that PAF has an early tendency for hypercoagulability, with the involvement of the intrinsic and extrinsic pathways of coagulation.

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)0.001
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 score0.345
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.189
GPT teacher head0.416
Teacher spread0.227 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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