Bibliographic record
Abstract
Because biomarkers to detect Alzheimer’s disease (AD) have not yet been validated, physicians must rely on clinical assessments. The Research Committee of the American Neuropsychiatric Association recommended that the ideal cognitive screening instrument have the following characteristics: First, it should take <15 minutes to administer by a clinician at any level of training. Second, it should sample all major cognitive domains, including memory, attention/concentration, executive function, visual-spatial skills, language, and orientation. Third, it should be reliable, having adequate test-retest and inter-rater validity. Finally, it should be able to detect cognitive disorders commonly encountered by neuropsychiatrists. The American Academy of Neurology Practice Parameters from 1994 and 2001 and the Canadian Consensus Guidelines on Dementia from 2007 recommend that physicians screen subjects with suspected dementia or mild cognitive impairment (MCI), since these patients are at increased risk for AD. Currently, there are no data on the utility of screening subjects who are asymptomatic. The most commonly used brief cognitive tests are the Mini-Mental State Examination (MMSE), according to a survey conducted by the International Psychogeriatric Association (IPA) in 2006, followed by the clock-drawing test, the delayed-word recall, the verbal fluency test, the similarities test, and the trail-making test. Clinicians appraised these assessments as the most effective and easiest to administer (Slide 1).
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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; both teacher heads agree on what is shown here.
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".