Functional assessment as a predictor of cognitive impairment: a retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: It is estimated that major neurocognitive disorders will affect 115.4 million people by 2050, representing a significant financial burden for society. Likewise, functional dependence for basic or instrumental activities of daily life is usually a consequence of the greater prevalence of disabling pathologies, and of the progressive decrease of the physiological reserve due to aging. OBJECTIVES: To determine the ratio between the functional test and prediction of deterioration of cognitive function in geriatric patients. To determine which factors are associated with decreased cognitive functions in elderly patients. METHODS: This is a retrospective cohort study. We included participants who attended the Geriatrics Outpatient Services of the Naval Medical Center of Peru from 2010 to 2014. Two groups were formed, the first comprised those who presented physiological loss of cognitive functions, defined as the decrease less than 4.1 points in the score of the Mini Mental State Examination during follow-up and a second group who had a pathological loss of cognitive functions, defined as a decrease of 4.2 points in average or more in the Mini Mental State Examination score during follow-up. Patients were followed for four years, performing functional evaluations, as well as collecting data on comorbidities and sociodemographic factors. RESULTS: We included 368 patients, whose mean age was 75.8 years and 50.91% male. Female gender (53.72%), poor education (62.81%), and comorbidities were statistically more frequent in the pathological cognitive decline group. Functional assessment variables had a worse disease progress through follow-up in the group with pathological disease progress of the Mini Mental State Examination score, including Timed Up and Go Test. CONCLUSIONS: Score values of Timed Up and Go Test manage to predict the risk of a pathological decrease in Mini Mental State Examination regardless of sociodemographic factors, comorbidities, and functional assessment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".