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Record W2160527970

Сравнительный анализ изменения толерантности к физической нагрузке у больных ишемической болезнью сердца под влиянием терапии, основанной на бисопрололе и ивабрадине

2013· article· ru· W2160527970 on OpenAlexaboutno aff
Е А Недоруба, А. Д. Багмет, Т В Таютина

Bibliographic record

VenueСовременные проблемы науки и образования · 2013
Typearticle
Languageru
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIvabradineBruce protocolStable anginaInternal medicineAspirinSinus rhythmCardiologyTreadmillAnginaBisoprololPhysical therapyCoronary artery diseaseAtrial fibrillationHeart rateHeart failureBlood pressureMyocardial infarction
DOInot available

Abstract

fetched live from OpenAlex

To conduct the study were selected 64 patients with ischemic heart disease (stable angina I-III functional class), patient age from 45 to 74 years. The diagnosis of angina I-III FC verified according to the treadmill test, as recommended by the Canadian Association of pits, cardiologists, clinical and anamnestic characteristics. A prerequisite to enroll patients in the study was the presence of sustained sinus rhythm and the absence of significant mitral valve regurgitation on the results of echocardiography. After defining the basic parameters examined patients were randomized into 2 groups. Patients first group (32 persons), in addition to standard therapy (aspirin, statins, ACE inhibitors, nitrates) for 12 weeks received a beta-blocker bisoprolol 5-10 mg / day. Patients second group (32 subjects), in addition to standard therapy for 12 weeks received If-inhibitor ivabradine at a dose of 5-7.5 mg 2 times a day. Terminating an ECG test was performed on a treadmill «Stress-Test ST-2001 (Netherlands) with the use of a modified Bruce protocol.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.003

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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
Published2013
Admission routes1
Has abstractyes

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