Identifying Patients at High and Low Risk of Cognitive Decline Using Rey Auditory Verbal Learning Test among Middle-Aged Memory Clinic Outpatients
Bibliographic record
Abstract
OBJECTIVES: To investigate whether application of cutoff levels in an episodic memory test (Rey Auditory Verbal Learning Test, RAVLT) is a useful method for identifying patients at high and low risk of cognitive decline and subsequent dementia. METHODS: 224 patients with memory complaints (mean age = 60.7 years, mean MMSE = 28.2) followed-up at a memory clinic over approximately 3 years were assigned retrospectively to one of three memory groups from their baseline results in RAVLT [severe (SIM), moderate (MIM) or no impairment (NIM)]. These groups were investigated regarding cognitive decline. RESULTS: Patients assigned to SIM showed significant cognitive decline and progressed to dementia at a high rate, while a normal performance in RAVLT at baseline (NIM) predicted normal cognition after 3 years. Patients with MIM constituted a heterogeneous group; some patients deteriorated cognitively, while the majority remained stable or improved. CONCLUSIONS: The application of cutoff levels in RAVLT at baseline showed that patients with severely impaired RAVLT performance were at a high risk of cognitive decline and progression to dementia, while patients with normal RAVLT results did not show cognitive decline during 3 years. Furthermore, the initial degree of memory impairment was decisive in the cognitive prognosis 3 years later.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".