Building National Patient Registries in Mexico: Insights from the MexOMICS Consortium
Bibliographic record
Abstract
ABSTRACT OBJECTIVE To introduce MexOMICS, a Mexican Consortium focused on establishing electronic databases to collect, cross-reference, and share health-related and omics data on the Mexican population. METHODS The Mexican Twin Registry (TwinsMX), Mexican Lupus Registry (LupusRGMX) and the Mexican Parkinson’s Research Network (Mex-PD) were designed and implemented using Research Electronic Data Capture web-based application. Registries were compiled through voluntary participation and on-site engagement with medical specialists. In some instances, DNA samples and Magnetic Resonance Imaging images were also acquired. RESULTS Since 2019, the MexOMICS Consortium has successfully established three electronic-based registries: TwinsMX (n=2915), LupusRGMX (n=1761) and Mex-PD (n=750). In addition to sociodemographic, psychosocial, and clinical data, MexOMICS has collected samples for genetic determinations across the three registries. Cognitive function assessments, conducted using the Montreal Cognitive Assessment, have been administered to a subsample of 376 Mex-PD participants. Furthermore, a subset of 267 twins underwent measurements of structural, functional, and spectroscopy brain images; comparable evaluations are projected for LupusRGMX and Mex-PD. CONCLUSIONS The MexOMICS registries offer a valuable repository of information concerning the potential interplay of genetic and environmental factors in health conditions among the Mexican population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".