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Prevalence and Clinical Implications of Anemia in Congestive Heart Failure Patients Followed at a Specialized

2006· article· en· W2020365795 on OpenAlexaff
Wael Elabbassi, Margaret Fraser, Kathryn Williams, Denise Cassan, Majed Haddad

Bibliographic record

VenueCongestive Heart Failure · 2006
Typearticle
Languageen
FieldMedicine
TopicErythropoietin and Anemia Treatment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineAnemiaHeart failureHemoglobinEmergency departmentInternal medicineIntensive care medicinePediatricsEmergency medicine

Abstract

fetched live from OpenAlex

The objective of this study was to assess the prevalence of anemia and to investigate its association with comorbidities and its impact on clinical outcomes in patients with heart failure. The association of predefined anemia, as well as the correlation of serum hemoglobin level as a continuous variable, with outcomes of emergency department visits, hospitalization, and mortality was investigated. There were fewer anemic patients in New York Heart Association classes I and II than in classes III and IV. Anemia was associated with higher rates of emergency department visits, hospital admissions, and all-cause mortality. Multivariable analysis showed that anemia is independently associated with mortality. When hemoglobin level was considered as a continuous variable, the authors noted that the mortality risk correlated with hemoglobin level disappears when hemoglobin level exceeds 140 g/L. The authors conclude that anemia has strong impacts on functional class and other clinical outcomes in patients with heart failure.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.305
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2006
Admission routes1
Has abstractyes

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