Presencia de deterioro cognitivo y topografía anátomo-clínica en pacientes con epilepsia en Cienfuegos
Bibliographic record
Abstract
Background: epilepsy is a pathological condition characterized by a recurrent non-provoked crisis, however, the presence of the crisis is a fraction of the global problem, patients with epilepsy develop a variety of neuropsychiatry problems, as cognitive affection, most of all, in the space of memory. Objective: evaluating the behavior of the cognitive deterioration and focalization according to anatomical- clinical topography in patients with epilepsy. Methods: a descriptive, correlational, cross-section and follow-up study of cases. The techniques used were: structured interview, the Montreal Cognitive Assessment's evaluation, and Luria´s neuropsychological exam. It was used SPDD statical parcel, version 1.5 to process the information that made possible the study of the obtained data, with the aim of expressing the results in chart of frequency and relation of variables in number and percent. Results: the 71.4 % of evaluated patients presented cognitive deterioration in any of its of measurement scales and they focalized according to neuropsychological exam. Conclusions: as the time of evolution of the disease increases, the frequency and duration of the crises, the grade of the cognitive deterioration in patients with epilepsy increases, focalizing with dysfunction majority fronto-temporary level according to anatomical-clinical topography.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".