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Record W2018714499 · doi:10.1136/emermed-2013-202874

Prehospital use of furosemide for the treatment of heart failure

2014· article· en· W2018714499 on OpenAlexaff
Andy Pan, Ian G. Stiell, Richard Dionne, Justin Maloney

Bibliographic record

VenueEmergency Medicine Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineAllergyCancerBladder cancerPopulationInternal medicineDermatologyPediatricsImmunologyEnvironmental health

Abstract

fetched live from OpenAlex

INTRODUCTION: The diagnosis and management of acute decompensated heart failure (HF) in the prehospital setting can be challenging. The objectives of this study are to evaluate the appropriateness of furosemide use by Emergency Medical Services (EMS) and its association with adverse outcomes. METHODS: This study was a multi-centre health records review of EMS patients who received prehospital furosemide or had an emergency department (ED) diagnosis of HF. We included acutely ill patients ≥50 years of age with shortness of breath transported by land EMS. Univariate and logistic regression analyses were performed to determine associations between furosemide use and serious adverse outcomes (acute renal failure, intubation, vasopressors or death). RESULTS: The study population consisted of 330 patients (N=58, furosemide given by EMS but no HF diagnosed in ED; N=110, furosemide given, HF diagnosed; N=162, no furosemide given, HF diagnosed). The median dose of intravenous furosemide was 80 mg (range 20-80 mg). Serious adverse outcomes occurred in 61 patients (19.0%, 23.6% and 14.8% of the three groups, respectively; p=0.18). The adjusted ORs for adverse events with furosemide use was 0.62 (95% CI 0.33 to 1.43) in patients with a diagnosis of HF and 1.14 (95% CI 0.58 to 2.23) in those without. CONCLUSIONS: More than a third of patients who received prehospital furosemide did not have an HF diagnosis, suggesting that the prehospital diagnosis of HF can be challenging. Serious adverse outcomes were identified in all patient groups and we found no statistically significant associations between furosemide use and adverse events.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0040.000

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 head0.048
GPT teacher head0.325
Teacher spread0.277 · 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.

Study designNot applicable
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

Citations8
Published2014
Admission routes1
Has abstractyes

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