Comparison of cognitive function tests AD8-INA against MoCA-INA and CDT in the elderly age group at the Uabau Health Center
Bibliographic record
Abstract
Background: Dementia is a syndrome of decreased cognitive ability that hinders daily life. Dementia is associated with mortality, especially in patients with major depression, so health services are needed to identify cognitive disorders through pre-elderly and elderly screening. AD8-INA is one of the cognitive screening methods where the questions are directed to patient caregivers that are most practical, concise, and suitable for patients who cannot use their dominant hand, so researchers are interested in studying the sensitivity and specificity of AD8-INA to MoCA-INA and CDT. Methods: A cross-sectional study design was used to observe and analyze cognitive function variables. This study used primary data sources with interviews based on questionnaires on 114 subjects, consisting of pre-elderly and elderly groups who met the eligibility criteria to be included in the study. The MoCA-INA scores were divided into 26 normal and 26 cognitive impairments, the CDT scores 4 normal and 4 cognitive impairments, and the AD8-INA scores 0–1 normal and 2 cognitive impairments. An analysis was carried out to determine the sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and confidence interval. Results: It was found that the sensitivity of AD8-INA to MoCA-INA was 87.14%, AD8-INA to CDT was 82.14%. In addition, the specificity of AD8-INA to MoCA-INA was 50% and AD8-INA to CDT was 61.11%. Conclusion: AD8-INA has high sensitivity but low specificity. AD8-INA can still be used as an initial screening tool for cognitive function at the Uabau Health Center, but all examination results must be evaluated further.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".