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Record W4213274741 · doi:10.1186/s13104-022-05938-z

Managing patients with heart failure: contemporary real-world experience

2022· article· en· W4213274741 on OpenAlexaff
Muhammad Siddiqui, Christopher Ripplinger, Hafsah Chalchal, D. Murthy

Bibliographic record

VenueBMC Research Notes · 2022
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsRegina General HospitalUniversity of SaskatchewanSaskatchewan HealthSaskatchewan Health Authority
Fundersnot available
KeywordsMedicineHeart failureGuidelineAngiotensin Receptor BlockersAngiotensin-converting enzymeMineralocorticoid receptorInternal medicineRetrospective cohort studyDiseaseEmergency medicineIntensive care medicineAldosteroneBlood pressure

Abstract

fetched live from OpenAlex

OBJECTIVE: Heart failure (HF) is a chronic disease with growing numbers of patients and a significant compromise in quality of life and high mortality. The main purpose of this study was to evaluate the current practices in managing patients with HF among patients admitted to the hospital and discharged with a primary diagnosis of HF and patients managed in the heart function clinic. RESULTS: This study is a retrospective chart review of patients admitted to the hospital and discharged with a primary diagnosis of HF. A total of 448 patient charts were reviewed, of which 173 patients were in the hospital group and 275 patients in the Clinic group. 278 (62.1%) were men, and 170 (37.9%) were women. The Clinic group of patients were significantly received guideline-directed medical therapy (Beta-blockers, Angiotensin-converting enzyme inhibitors, Angiotensin receptor blockers, Diuretics, Mineralocorticoid receptor antagonists-p < 0.001). The Clinic group of patients (17.1%) were significantly less re-hospitalized (p < 0.001) compared to the Hospital group (28%) at 180 days. Physician led multidisciplinary Heart function clinics have better adherence to guideline directed medical therapy and significantly lower rates of re-hospitalization thereby providing cost effective heart failure management with usual care.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.470
Threshold uncertainty score0.947

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.134
GPT teacher head0.393
Teacher spread0.259 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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