Detección del deterioro cognitivo leve con la Batería Neuropsicológica Computarizada de Tamizaje
Bibliographic record
Abstract
Objective: The purpose of the present research was to validate the use of the effectiveness of the Batería Neuropsicológica Computarizada de Tamizaje(BNCT) to detect Mild cognitive impairment (MCI) compared to a gold-standard neuropsychological assessment. We also compared the diagnostic accuracy of the BNCT with another frequently used screening test: the Montreal Cognitive Assessment (MoCA). Methods: Using a cross-sectional design and an intentional sampling we selected 23 elderly adults who completed a neuropsychological assessment and who were diagnosed as MCI. The average age was 74.7 years, all presented subjective complaints of cognitive impairment not of enough severity as tointerfere with their instrumental activities for daily living and fulfilled the diagnostic clinical criteria of MCI according to Petersen (1999). We compared the diagnostic accuracy of the BNCT and the MoCA with the results obtained by the neuropsychological battery.Results:According to the complete neuropsychological evaluation the 23 patients were classified as MCI.In the BNCT 91% of the patients were classified with MCI and showed alterations delay recall (56%), time orientation (48%), sequential drawing(43%), verbal memory (30%) and opposite reactions (26%). MoCA classified as MCI 79% of the patients and negative 21% of the sample. Tasks that predicted MCI were delay recall (81%), drawing of a cube (52%), clock drawing (47%), digitspan (38%), subtraction (24%), phonological fluency (19%) andtrial making (14%).Patients who were wrongly classified had loweducational level (lessthan 5 years of schooling experience). Accordingto the results of the complete neuropsychological battery, the BNCT provides better classification of subjects with MCI than the MoCA, since it uses norms according to age and education, while the MoCA does not consider these variables.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".