Deterioro Cognitivo Leve: detección y costo de enfermedad en Perú
Bibliographic record
Abstract
Estimada Editora: Unas prevalencias elevadas de deterioro cognitivo leve (DCL), superiores al 50%, como la descrita en Perú por Zegarra-Valdivia,1 suscitan cuestionamientos sobre si se ha identificado adecuadamente este evento y cuál ha sido su impacto económico para la sociedad. Para responder a la primera pregunta, se debe verificar que el instrumento empleado sea el apropiado. Por lo que, en términos de validación y rendimiento, el estudio utiliza 2 de las 3 pruebas previamente validadas en Perú: Mini-Mental State Examination y Clock Drawing Test, con rendimientos aceptables a buenos (AUC 0,65 – 0,85 y 0,69, respectivamente).2 Situación que podría optimizarse con el uso de pruebas breves que valoren simultáneamente varias áreas cognitivas, ejemplo: Montreal Cognitive Assessment, que obtuvo un mejor rendimiento en otros países de Latinoamérica (AUC 0,76 – 0,93), pero que requiere evaluación en Perú
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".