Research, diagnosis and education in inborn errors of metabolism in Colombia: 20 years’ experience from a reference center
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".