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Record W2548651282 · doi:10.14740/cr494w

Effects of Cilnidipine on Heart Rate and Uric Acid Metabolism in Patients With Essential Hypertension

2016· article· en· W2548651282 on OpenAlexvenueno aff
Arijit Das, Prakash Kumar, Abha Kumari, Satish Chandra, Manju Gari, Nidhi Singh, Debleena Dey

Bibliographic record

VenueCardiology Research · 2016
Typearticle
Languageen
FieldMedicine
TopicGout, Hyperuricemia, Uric Acid
Canadian institutionsnot available
Fundersnot available
KeywordsAmlodipineMedicineHyperuricemiaEssential hypertensionCalcium channel blockerInternal medicineUric acidCardiologyHeart rateCalcium channelEndocrinologyCalciumBlood pressure

Abstract

fetched live from OpenAlex

BACKGROUND: The relation between hypertension and hyperuricemia has been established by epidemiological studies. Calcium channel blockers are one of the first-line drugs for newly diagnosed patients with essential hypertension. Cilnidipine is a new calcium channel blocker acting by blocking both L- and N-type calcium channels. The aim of this study was to compare the effectiveness of amlodipine and cilnidipine in patients with essential hypertension and their effects on heart rate and serum uric acid levels. METHODS: Out of 100 enrolled patients, 92 completed the study. They were randomly assigned to amlodipine (N = 47) and cilnidipine (N = 45) groups. Cilnidipine was started at 10 mg/day and then adjusted to 5 - 20 mg/day, and amlodipine was started at 5 mg/day and then adjusted to 2.5 - 10 mg/day. RESULTS: After 24 weeks of study, patients in cilnidipine groups showed significant reduction in heart rate and serum uric acid levels from baseline (P = 0.00). CONCLUSION: In clinical setting where both hypertension and hyperuricemia exist, cilnidipine can be a promising drug of choice.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.526
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.017
GPT teacher head0.290
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 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

Citations15
Published2016
Admission routes1
Has abstractyes

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