MétaCan
Menu
Back to cohort

Diagnóstico y manejo de las complicaciones cardiacas en el paciente con distrofia muscular de Duchenne

2025· article· es· W4411136921 on OpenAlexaff
Manuel Huertas Quiñones, Fernando Suárez‐Obando, Norma Carolina Barajas Viracachá, P. Ortiz, Edna Julieth Bobadilla Quedada, Carlos Ernesto Bolaños Almeida, José Manuel Cañón Zambrano, Sandra Milena Castellar-Leones, J. Hernández, Juan David Lasprilla Tovar, Nicolas J. Laza Gutierrez, Isabel Londoño Ossa, Blair Ortiz, Fernando Ortiz‐Corredor, Sandra Yaneth Ospina Lagos, Juan Carlos Prieto, Carolina Rivera‐Nieto, Edicson Ruiz Ospina, Felipe Ruiz-Botero, Maria Salcedo-Maldonado, Diana Pilar Soto Peña, Lina Marcela Tavera-Saldaña, María Julia Torres-Nieto, Diana Carolina Sánchez-Peñarete

Bibliographic record

VenueRevista Ciencias de la Salud · 2025
Typearticle
Languagees
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMisericordia Community Hospital
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

La distrofia muscular de Duchenne (DMD) es una condición hereditaria, grave y progresiva que afecta la musculatura causando daño progresivo y debilidad posterior. Es la distrofia muscular más frecuente y severa en la infancia. Conforme progresa la enfermedad, se evidencia compromiso del músculo cardiaco con la presencia de miocardiopatía dilatada y miocardiopatía hipertrófica o restrictiva que conllevan a falla cardiaca. Actualmente, no se cuenta con un tratamiento curativo para la DMD o sus complicaciones cardiovasculares. Sin embargo, el uso de glucocorticoides, la rehabilitación física, la ventilación mecánica no invasiva y el manejo multidisciplinario con un enfoque cardiorrespiratorio y ortopédico han evidenciado una modificación de la historia natural de la enfermedad. El presente artículo proporciona una síntesis sobre el diagnóstico y tratamiento de los pacientes con diagnóstico de distrofia muscular de Duchenne que presentan complicaciones cardiovasculares.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.700
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.004
GPT teacher head0.290
Teacher spread0.286 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueRevista Ciencias de la SaludSame topicMuscle Physiology and DisordersFrench-language works237,207