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Record W4281857523 · doi:10.26852/28059107.562

Perfil psicosocial de pacientes atendidos en una unidad de insuficiencia cardíaca y trasplante cardíaco

2022· article· es· W4281857523 on OpenAlexaboutno aff
Mónica Becerra-Niño, Carolina Hernández-Pinzon, Diana Molano-Barrera, Freddy Mendivelso Duarte, Carlos Arias-Barrera

Bibliographic record

VenueMeridiano - Revista Colombiana de Salud Mental · 2022
Typearticle
Languagees
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHumanitiesArt

Abstract

fetched live from OpenAlex

Este estudio descriptivo de corte transversal tiene como objetivo describir el perfil psicosocial de los pacientes atendidos durante el periodo de septiembre de 2011 a septiembre de 2013 en la Unidad de Insuficiencia Cardíaca y Trasplante Cardíaco de laClínica Universitaria Colombia de la ciudad de Bogotá. La muestra fue de 185 pacientes a quienes se les aplicó el Cuestionario de Salud PHQ-9, Evaluación Cognitiva de Montreal - MoCA, APGAR familiar y cuestionario de apoyo social de DUKE - UNC. Eventualmente se encontró que el 22,5% presentó síntomas de depresión leve, el 54,1% mostró disfunción cognitiva leve, el 94% contaba con una red de apoyo social adecuada y el 92,4% contaba con una familia funcional. Se concluye que los aspectos psicológicos y la disponibilidad de una redde apoyo familiar y social juegan un papel importante en el proceso salud- enfermedad de la persona con insuficiencia cardiaca, pudiendo afectar positiva o negativamente en el manejo médico, las conductas de autocuidado y en el mantenimiento de la calidad de vida. Asimismo es necesario que el equipo de salud incluya al paciente y familia en el diseño e implementación del plan de atención para lograr cumplir las metas terapéuticas.

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)0.003
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 score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.308
Teacher spread0.298 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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