Comparative Performance of Five Cognitive Screening Tests in a Large Sample of Seniors
Bibliographic record
Abstract
INTRODUCTION: Recent introductions of disease-modifying treatments for Alzheimer's disease have re-invigorated the cause of early dementia detection. Cognitive "paper and pencil" tests represent the bedrock of clinical assessment, because they are cheap, easy to perform, and do not require brain imaging or biological testing. Cognitive tests vary greatly in duration, complexity, sociolinguistic biases, probed cognitive domains, and their specificity and sensitivity of detecting cognitive impairment (CI). Consequently, an ecologically valid head-to-head comparison seems essential for evidence-based dementia screening. METHOD: We compared five tests: Montreal cognitive assessment (MoCA), Alzheimer's disease assessment scale-cognitive subscale (ADAS), Addenbrooke's cognitive examination (ACE-III), euro-coin handling test (Eurotest), and image identification test (Phototest) on a large sample of seniors (N = 456, 77.9 ± 8 years, 71% females). Their specificity and sensitivity were estimated in a novel way by contrasting each test's outcome to the majority outcome across the remaining tests (comparative specificity and sensitivity calculation [CSSC]). This obviates the need for an a priori gold standard such as a clinically clear-cut sample of dementia/MCI/controls. We posit that the CSSC results in a more ecologically valid estimation of clinical performance while precluding biases resulting from different dementia/MCI diagnostic criteria and the proficiency in detecting these conditions. RESULTS: There exists a stark trade-off between behavioral test specificity and sensitivity. The test with the highest specificity had the lowest sensitivity, and vice versa. The comparative specificities and sensitivities were, respectively: Phototest (97%, 47%), Eurotest (94%, 55%), ADAS (90%, 68%), ACE-III (72%, 77%), MoCA (55%, 95%). CONCLUSION: Assuming a CI prevalence of 10%, the shortest (∼3 min) and the simplest instrument, the Phototest, was shown to have the best overall performance (accuracy 92%, PPV 66%, NPV 94%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".