Digital screening for cognitive decline in the Spanish language for older adults
Bibliographic record
Abstract
BackgroundRecent technological advances in digital assessment of auditory and cognitive function may be used to circumvent the costs associated to screening for cognitive decline in the general population. Pre-clinical cognitive screening could have a transformative impact for preventive care in our increasingly old world population. However, advances in digital assessment need to be adapted for Spanish-speaking populations as innovation occurs mainly in English.ObjectiveThis study explores the potential of a novel screening battery for cognitive decline that utilizes digital tests of cognitive and auditory function adapted to the Spanish language.MethodsParticipants were evaluated on standard clinical scales and questionnaires, and on a digital battery of auditory and cognitive tests with potential clinical value to screen cognitive decline.ResultsWe report the ability to detect minimal cognitive impairment (MCI; 3/10 tests) and dementia (10/10 tests) of each digital test and the full battery. We further show concurrent validity for cognitive screening with the Montreal Cognitive Assessment (MoCA) and describe the shared variance across tests in the battery. Lastly, we show multiple regression models predicted with medium sensitivity (57%) and high specificity (97%) the dementia cases, and with high sensitivity (93%) but low specificity (31%) the MCI cases.ConclusionsOverall, this study demonstrates discriminatory value and concurrent validity to screen for cognitive decline in older adults using open-access digital auditory and cognitive tests in the Spanish language. Follow-up studies with larger and more diverse samples will be instrumental in achieving cognitive screening procedures for the general population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".