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Record W4205934472 · doi:10.29277/cardio.36.3.3

Impacto de las políticas de salud en la incidencia de infarto de miocardio durante la pandemia por COVID-19 en Uruguay

2021· article· es· W4205934472 on OpenAlexaff
Víctor Dayan, Abayubá Perna, Enrique Soto, Dayan De Marzo, Dres Dayan, Álvaro Niggemeyer, Alejandro Cuesta, Natalia Piñeiro, Graciela Fernández, Rosana Gambogi

Bibliographic record

VenueRevista Uruguaya de Cardiología · 2021
Typearticle
Languagees
FieldHealth Professions
TopicHealth and Lifestyle Studies
Canadian institutionsImpact
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)MedicinePolitical scienceHumanitiesVirologyArtOutbreakInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Introduccin: las medidas sanitarias de emergencia impuestas para contener el SARS-CoV-2 pueden tener efectos colaterales en la atencin de enfermedades cardiovasculares. Los datos mundiales de los pases sobre la incidencia de infarto agudo de miocardio con elevacin del segmento ST (IAMCEST) durante la pandemia son fundamentales para la poltica sanitaria futura. Objetivos: nuestro objetivo fue determinar si las medidas sanitarias de emergencia impuestas en Uruguay tuvieron un impacto directo en la calidad de la atencin en la reperfusin del IAMCEST. Mtodos: realizamos un estudio retrospectivo poblacional de todo el pas para determinar la incidencia de reperfusin de IAMCEST (fibrinolticos e intervencin coronaria percutnea, FBL e ICP respectivamente) durante el perodo sanitario de emergencia. La tasa de incidencia de la reperfusin, el tiempo hasta la reperfusin y la mortalidad asociada se recopilaron de la base de datos del Fondo Nacional de Recursos (organizacin gubernamental nica a cargo de la financiacin de la reperfusin del IAMCEST en Uruguay). Estos mismos datos se recuperaron para 2019, 2018 y 2017

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)0.022
metaresearch head score (Gemma)0.057
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.454
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.057
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0030.005
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 head0.031
GPT teacher head0.433
Teacher spread0.402 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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