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Record W2372406444

Therapeutic effect of nicorandil on stable coronary heart disease

2014· article· en· W2372406444 on OpenAlexaboutno aff
Shan Fu-xian

Bibliographic record

VenueXinxueguan kangfu yixue zazhi · 2014
Typearticle
Languageen
FieldMedicine
TopicCardiac Ischemia and Reperfusion
Canadian institutionsnot available
Fundersnot available
KeywordsNicorandilMedicineTherapeutic effectAnginaInternal medicineCardiologyCoronary heart diseaseC-reactive proteinCanadian Cardiovascular SocietyStable anginaMyocardial infarctionInflammation
DOInot available

Abstract

fetched live from OpenAlex

Objective:To explore the therapeutic effect of nicorandil based on routine medication on stable coronary heart disease(CHD).Methods:A total of 100 inpatients diagnosed as stable CHD were enrolled,randomly and equally divided into nicorandil group(received nicorandil 5mg based on routine medication,three times/d)and routine treatment group.After discharge,patients were followed up for six months.Angina pectoris frequency,ECG,serum level of high sensitive C reactive protein(hsCRP)and 6min walking distance(6MWD)were compared between two groups before and after follow up.Results:After six-month follow up,compared with routine treatment group,there were significant improvements in clinic therapeutic effect(48% vs.74%)and ECG therapeutic effect(78% vs.96%)in nicorandil group,P0.05all;compared with before follow-up,there were significant reductions in angina pectoris frequency and serum hsCRP level;and significant rise in 6MWD in both groups,P0.05all;compared with routine treatment group,there were significant reductions in angina pectoris frequency[(10.35±1.51)times/week vs.(9.95±1.65)times/week]and hsCRP level[(1.12±0.51)mg/L vs.(0.95±0.43)mg/L];and significant increase in 6MWD [(342.38±35.64)m vs.(388.64±32.43)m]in nicorandil group,P0.05 all.Conclusion:Nicorandil can effectively reduce the attack number of angina pectoris of stable coronary heart disease and serum hsCRP level,increase exercise tolerance and improve clinical therapeutic effect.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.754

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.255
Teacher spread0.248 · 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 designBench or experimental
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
Published2014
Admission routes1
Has abstractyes

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