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Record W3179890373 · doi:10.24875/acm.20000508

Leishmaniasis y corazón

2021· article· es· W3179890373 on OpenAlexaff

Bibliographic record

VenueArchivos de cardiología de México · 2021
Typearticle
Languagees
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsVisceral leishmaniasisDiseaseTropical diseaseLeishmaniasisCardiotoxicityNeglected tropical diseases

Abstract

fetched live from OpenAlex

As one of the neglected tropical diseases, leishmaniasis is defined as a parasitic communicable disease that is most prevalent in tropical and subtropical regions, affecting especially populations living in poverty. It has a profound negative impact on developing economies. It represents a group of heterogeneous syndromes with a wide spectrum of severity ranging from self-resolving cutaneous injuries to disseminated visceral compromise. Visceral leishmaniasis represents its most severe form, can affect almost all organs, and can have fatal consequences, especially in immunosuppressed patients. Cardiac involvement seems to be rare but has not been deeply studied. Consequently, there are no clear recommendations for the screening of cardiac manifestations in these patients. However, cardiovascular complications could be potentially lethal. In addition, there are valuable reports on the potential cardiotoxicity caused by drugs used in the treatment of this condition, so knowledge of its side effects could have important implications. This article is a part of the "Neglected Tropical Diseases and other Infectious Diseases affecting the Heart" project (the NET-Heart Project); its purpose is to review all the information available regarding cardiac implications of this disease and its treatment and to add knowledge to this field of study, focusing on the barriers for diagnosis and treatment, and how to adopt strategies to overcome them.

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)0.003
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.029
GPT teacher head0.313
Teacher spread0.284 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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