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 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".