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Record W2887704123 · doi:10.1186/s13023-018-0879-2

Research, diagnosis and education in inborn errors of metabolism in Colombia: 20 years’ experience from a reference center

2018· review· en· W2887704123 on OpenAlexfundno aff
Olga Yaneth Echeverri-Peña, Johana Guevara, Ángela J. Espejo, Andrea Ardila, Ninna Pulido, Magda Reyes, Alexander Rodríguez‐López, Carlos Javier Alméciga-Díaz, Luis Alejandro Barrera

Bibliographic record

VenueOrphanet Journal of Rare Diseases · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolism and Genetic Disorders
Canadian institutionsnot available
FundersPontificia Universidad JaverianaDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)Craft Ontario
KeywordsMedicineHuman geneticsInclusion (mineral)Translational researchHealth professionalsMEDLINEFamily medicineMedical educationHealth carePathologyPolitical sciencePsychologyBiology

Abstract

fetched live from OpenAlex

The use of specialized centers has been the main alternative for an appropriate diagnosis, management and follow up of patients affected by inborn errors of metabolism (IEM). These centers facilitate the training of different professionals, as well as the research at basic, translational and clinical levels. Nevertheless, few reports have described the experience of these centers and their local and/or global impact in the study of IEM. In this paper, we describe the experience of a Colombian reference center for the research, diagnosis, training and education on IEM. During the last 20 years, important advances have been achieved in the clinical knowledge of these disorders, as well as in the local availability of several diagnosis tests. Organic acidurias have been the most frequently detected diseases, followed by aminoacidopathies and peroxisomal disorders. Research efforts have been focused in the production of recombinant proteins in microorganisms towards the development of new enzyme replacement therapies, the design of gene therapy vectors and the use of bioinformatics tools for the understanding of IEM. In addition, this center has participated in the education and training of a large number professionals at different levels, which has contributed to increase the knowledge and divulgation of these disorders along the country. Noteworthy, in close collaboration with patient advocacy groups, we have participated in the discussion and construction of initiatives for the inclusion of diagnosis tests and treatments in the health system.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.955
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.369
Teacher spread0.328 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations19
Published2018
Admission routes1
Has abstractyes

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