Diabetes mellitus en pacientes con enfermedad de Alzheimer: descripción clínica y correlación con el genotipo APOE en una muestra de población del departamento de Antioquia, Colombia
Bibliographic record
Abstract
Introduction. Alzheimer’s disease is a multifactorial disease affecting approximately twenty million people worldwide. Numerous variables are associated with increased risk of developing this severe neurological disorder. Among the risk factors, diabetes mellitus, and the ε4 isoform of the APOE gene have been amply demonstrated as increasing the risk of developing this disease.Objective. To determine if a correlation exists between APOE genotype, diabetes mellitus and Alzheimer’s disease.Materials and methods. Clinical studies were carried out by surveying the clinical histories in a group of patients in the province of Antioquia, Colombia. Forty-three Alzheimer’s patients were compared with 43 control subjects, paired by age and gender. Commercially available methods were used to determine whether the patients had diabetes, and restriction enzyme-based genotyping was used to determine the APOE genotypes.Results. The most common non-neurological comorbidities were: arterial hypertension, acute myocardial infarction, chronic obstructive pulmonary disease and hypothyroidism. From the many variables investigated, two were conclusive: (1) the presence of Alzheimer’s disease was higher in patients with diabetes mellitus, and (2) no correlation between late-onset sporadic Alzheimer’s disease and APOE was found in the target population.Conclusions. To detect any association with the APOE genotype, a study involving much a larger population samples must be undertaken.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".