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Record W2394087357

Analysis of the Changes of Plasma NT-pro BNP Concentration in Elderly Patients with Heart Failure of Different Severity and the Prognosis

2015· article· en· W2394087357 on OpenAlexaboutno aff
Li Min

Bibliographic record

VenueChina Foreign Medical Treatment · 2015
Typearticle
Languageen
FieldMedicine
TopicMedical Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHeart failureEjection fractionInternal medicineCardiologyAnginaCanadian Cardiovascular SocietyCause of deathStatistical significanceDiseaseMyocardial infarction
DOInot available

Abstract

fetched live from OpenAlex

Objective To explore the changes in plasma NT-pro BNP concentration in elderly patients with heart failure of different severity and the prognosis. Methods The plasma NT-pro BNP concentration was measured in 206 patients with heart failure of different severity admitted from March 2012 to December 2013 and divided into the death group and the survival group. The death group included patients with angina pectoris, recurrence of heart failure and the death cases, while the survival group did not include those with angina pectoris, recurrence of heart failure and death cases. And the changes of plasma concentration and prognosis were compared between the two groups. Results The result of the data analysis showed that for patients with different causes of disease, the plasma NT-pro BNP concentrations were not the same. Compared with those without cardiovascular disease, patients with cardiovascular disease had lower left ventricular ejection fraction, with statistical significance(P0.05). The data showed that the plasma concentration of patients in the death group on admission was significantly higher than that of those in the survival group. Conclusion The analysis of the data showed that the plasma concentration of the survival group was lower than that of the death group. If the plasma concentration in patients with heart failure continues to rise, the prognosis of the patients should be paid attention, which is of great research value in clinical practice.

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)0.001
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 score0.017
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.023
GPT teacher head0.281
Teacher spread0.258 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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