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Record W2469586099 · doi:10.14740/cr469w

Early Changes in the Antithrombin and Thrombin-Antithrombin Complex in Patients With Paroxysmal Atrial Fibrillation

2016· article· en· W2469586099 on OpenAlexvenueno aff
Мariya Negreva, Svetoslav Georgiev, Krasimira Prodanova, Julia Nikolova

Bibliographic record

VenueCardiology Research · 2016
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiologyParoxysmal atrial fibrillationAntithrombinInternal medicineAtrial fibrillationThrombinHeparinPlatelet

Abstract

fetched live from OpenAlex

BACKGROUND: Data on coagulation changes in paroxysmal atrial fibrillation (PAF) are scarce. The aim of this study was to examine plasma antithrombin (AT) levels and activity as well as thrombin-antithrombin (TAT) complex levels in the early hours of the clinical manifestation of PAF. METHODS: Fifty-one patients (26 men and 25 women; mean age 59.84 ± 1.60 years) were consecutively selected with PAF duration < 24 hours, and 52 controls (26 men and 26 women; mean age 59.50 ± 1.46 years) matched the patients in terms of gender, age and comorbidities. Plasma levels and activity of AT and levels of the covalent TAT complex were studied once in each study participant. RESULTS: AT plasma levels in PAF patients were statistically significantly lower compared to controls (164.69 ± 10.51 vs. 276.21 ± 8.29 μg/mL, P < 0.001). Plasma activity of the anticoagulant was also significantly lower in PAF (71.33±4.87 vs. 110.72±3.09%, P < 0.001). TAT complex concentration in plasma was higher in the patient group (5.32 ± 0.23 vs. 3.20 ± 0.14 μg/L, P < 0.001). CONCLUSION: We can say that PAF is associated with significantly reduced AT levels and activity and increased levels of TAT complex during the first 24 hours after its manifestation. These changes indicate a reduced activity of AT anticoagulant system, which is a probable prerequisite for the established enhanced coagulation (high TAT complex levels).

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)0.000
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 score0.008
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.144
GPT teacher head0.389
Teacher spread0.244 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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