Patient‐specific cognitive profiles in the detection of dementia subtypes: A proposal
Bibliographic record
Abstract
Many physicians rely on sum score cognitive screening tests to evaluate patients for cognitive decline. Because the vast majority of cognitively impaired patients never receive more extensive testing, the results of these screening tests impact patients and their family members profoundly. No previous study has examined whether the metrics used by the popular Mini-Mental State Examination, Montreal Cognitive Assessment, and Saint Louis University Mental Status tests reliably identify single-domain deficits or allow clinicians to adequately track disease progression. We compare side by side the metrics used by these three tests to highlight the differences in the ways they measure domain impairments. We then contrast the sum score approach to cognitive screening with brief domain-specific tests that use extended metrics in each domain examined. Last, we suggest that moderate-to-severe domain-specific deficits on these tests should lead physicians to anticipate specific functional problems and alert family members.
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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 | 0.040 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".