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Record W2966370147 · doi:10.14740/cr898

Importance of Basal Metabolic Index in the Diagnosis of Heart Failure With B-Type Natriuretic Peptide

2019· article· en· W2966370147 on OpenAlexvenueno aff
Waqas Ullah, Asrar Ahmad, Yasar Sattar, Usman Sarwar, Hafez Mohammad Ammar Abdullah, Muhammad Arslan Cheema, Vincent M. Figueredo

Bibliographic record

VenueCardiology Research · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHeart failureNatriuretic peptideInternal medicineExacerbationCardiologyBody mass indexBasal (medicine)Brain natriuretic peptidePopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Increased basal metabolic index (BMI) is associated with decreased levels of B-type natriuretic peptide (BNP). This makes the diagnosis of the congestive heart failure challenging in the obese population. We sought to determine the association and strength of the relationship between the two variables. METHODS: The association between BMI and BNP was examined in 405 patients utilizing a retrospective chart review in a single center study. Pearson correlation and regression analyses were performed to identify trends. BNP trends were also correlated with age. RESULTS: The mean age of patients was 77 years with 45% men and 55% women. Mean BNP level was 1,158 standard deviation (SD) ± 1,537. Mean BMI was 33 SD ± 28. BNP levels were found to be inversely related to increasing BMI (P value < 0.001). Using a cut-off of 3,500 pg/mL, there was a linear negative correlation on the dotted graph. In regression analysis the measure of effect of BMI on BNP levels was -0.90 pg/mL. There was no significant association between age and BNP levels (P = 0.90). CONCLUSIONS: Irrespective of age, obese patients have lower BNP levels, complicating the diagnosis of heart failure exacerbation in such patients. Our results suggest that BNP levels in patients with BMI greater than 33 should be adjusted 9 pg/mL per unit increase in BMI.

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 score0.022
Threshold uncertainty score0.295

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 integrity0.0000.001
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 head0.034
GPT teacher head0.332
Teacher spread0.297 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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