Sensitive written hedgehog <i>PIC</i> ture <i>N</i> aming and <i>I</i> mmediate <i>R</i> ecall ( <i>PICNIR</i> ) as a valid and brief test of semantic and short-term episodic memory for very mild cognitive impairment
Bibliographic record
Abstract
BACKGROUND: Semantic and short-term episodic memory are impaired in some brain disorders including Alzheimer's disease. OBJECTIVE: Development and validation of an almost self-administered, but cognitively demanding four-minute test identifying very mild cognitive impairment (vMCI). METHODS: ) consisted of two parts. The first task was to write down the names of 20 black-and-white pictures to evaluate long-term semantic memory and language. The second task involves immediate recall and writing the names of as many previously named pictures as possible in one minute. The PICNIR is assessed using the number of naming errors (NE) and correctly recalled picture names (PICR). The PICNIR and a neuropsychological battery were administered to 190 elderly individuals living independently in the community. They were divided into those with vMCI (n = 43 with Montreal Cognitive Assessment (MoCA) 24 ± 3 points) and sociodemographically matched cognitively normal (CN) individuals (n = 147 with MoCA 26 ± 3). Both subgroups had predicted mean Mini-Mental State Examination scores of 28-29 points. RESULTS: < 0.000001). Discriminative validity was satisfactory using the area under the ROC curve (AUC): 0.76 for PICR, 0.74 for MoCA, 0.67 for MoCA-five-word recall, and 0.59 for NE. The AUCs of PICR and MoCA were comparable and larger than those of MoCA five-point recall or NE. Logical Memory scores, RAVLT scores, Digit symbol, and animal fluency correlated with PICR. CONCLUSIONS: The picture-based PICNIR is an ultra-brief, sensitive cognitive test valid for assessing very mild cognitive impairment. Its effectiveness should be validated for other languages and cultures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".