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Record W2749913250 · doi:10.4330/wjc.v9.i8.685

Effects of hypertonic saline solution on body weight and serum creatinine in patients with acute decompensated heart failure

2017· article· en· W2749913250 on OpenAlexaff
Gabrielle Lafrenière, Patrick Béliveau, Jean-Yves Bégin, David Simonyan, Sylvain Côté, Valérie Gaudreault, Zeev Israeli, Shahar Lavi, Rodrigo Bagur

Bibliographic record

VenueWorld Journal of Cardiology · 2017
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsWestern UniversityUniversité LavalCentre hospitalier universitaire de QuébecUniversité du Québec à Trois-RivièresLondon Health Sciences Centre
Fundersnot available
KeywordsMedicineAcute decompensated heart failureFurosemideCreatinineRenal functionEjection fractionHeart failureWeight lossHypertonic salineInternal medicineUrologyObesity

Abstract

fetched live from OpenAlex

AIM: To test the safety and effectiveness of hypertonic saline solution (HSS + F) as a strategy for weight loss and prevention of further deterioration of renal function. METHODS: Patients admitted with acute decompensated heart failure (ADHF) who received HSS + F were included in the study. After a period of a standard ADHF treatment, our patients received an intravenous infusion of furosemide (250 mg) combined with HSS (150 mL of 3% NaCl) twice a day for a mean duration of 2.3 d. Our primary outcomes were weight loss and a change in serum creatinine per day of treatment. The parameters of the period prior to treatment with HSS + F were compared with those of the period with HSS + F. RESULTS: = 0.0168). Importantly, the change in creatinine was not significantly different. CONCLUSION: This study supports the effectiveness of HSS + F on weight loss in patients with ADHF. The safety profile, particularly with regard to renal function, leads us to believe that HSS + F may be a valuable option for those patients presenting with ADHF who do not respond to conventional treatment with intravenous furosemide alone.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.344

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 head0.005
GPT teacher head0.247
Teacher spread0.241 · 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

Citations26
Published2017
Admission routes1
Has abstractyes

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