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Interpretación de pruebas genéticas y biomarcadores en la distrofia muscular de Duchenne

2025· article· es· W4409683594 on OpenAlexaff
Fernando Suárez‐Obando, Carolina Rivera Nieto, Norma Carolina Barajas Viracachá, P. Ortiz, Edna Julieth Bobadilla-Quesada, Carlos Ernesto Bolaños Almeida, José Manuel Cañón Zambrano, Sandra Milena Castellar-Leones, Manuel Huertas Quiñones, José L. Hernández, Juan David Lasprilla Tovar, Isabel Londoño Ossa, Sergio Nossa, Blair Ortiz, Fernando Ortiz‐Corredor, Sandra Yaneth Ospina Lagos, Juan Carlos Prieto, 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
KeywordsMedicineDuchenne muscular dystrophyInternal medicine

Abstract

fetched live from OpenAlex

Entre las principales alteraciones que caracterizan la distrofia muscular de Duchenne (DMD) se encuentran: daño de las fibras musculares durante la contracción, daño muscular crónico subsecuente, inflamación y posterior reemplazo de las fibras musculares por tejido fibroso. Este tipo de alteraciones puede reflejarse a través de biomarcadores de la enfermedad. Los biomarcadores en DMD son útiles para hacer el diagnóstico, el seguimiento y la evaluación de la respuesta al tratamiento. La indicación para solicitar los distintos biomarcadores varía de acuerdo con la edad y la historia natural de la enfermedad y su correcta utilización permite realizar un enfoque terapéutico adecuado, un seguimiento correcto y una rehabilitación satisfactoria. En la presente revisión se describen los diferentes tipos de biomarcadores y métodos diagnósticos utilizados en pacientes con DMD, y se recomienda su adecuada utilización de acuerdo con la edad y la historia natural de la enfermedad.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.003
GPT teacher head0.283
Teacher spread0.280 · 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 designBench or experimental
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