A comparison of the Lille Apathy Rating Scale-informant, the Apathy Evaluation Scale-Informant, and the Neuropsychiatric Inventory-Apathy Subscale in mild cognitive impairment informants
Bibliographic record
Abstract
Objective To compare the Lille Apathy Rating Scale-informant (LARS-i), the Apathy Evaluation Scale-Informant (AES-I) and the Neuropsychiatric Inventory-Apathy Subscale (NPI-Apathy) in mild cognitive impairment (MCI) informants. Methods Data were collected from 181 informants of patients with MCI by the LARS-i, AES-I and NPI-Apathy scales. The evaluation results by the three scales were analyzed by correlation analysis and receiver operating characteristic (ROC) curve. Results By the Chinese versions of the LARS-i and AES-I scales, the best ROC curve was at a cutoff score of -16, 33 respectively for a prediction of apathy (with sensitivity of 72.00%, 75.31% respectively, and specificity of 65.38%, 75.76% respectively). The scores of the LARS-i and AES-i scales (r=0.566, P<0.01), and the scores of AES-I and NPI-Apathy scales (r=0.388, P<0.01) were positively correlated. The AES-I and NPI-Apathy scores were positively correlated with the scores of the Geriatric Depression Scale (r=0.250, 0.169, P<0.05), while the score of the LARS-i scale not. The scores of LARS-i, AES-I scales were negatively correlated with the score of MoCA scale (r=-0.232, -0.239, P<0.01), whereas the NPI-Apathy score had no correlation with the MoCA score. The AES-I and LARS-i scales reported greater apathy compared to that reported by the NPI-Apathy scale, with cases of 94, 81, 25 respectively. Conclusions The results of the Chinese versions of LARS-i and AES-I scales are similar, which are better than the NPI-Apathy scale in MCI informants in China. And the LARS-i scale can distinguish between apathy and depression to a certain extent. Key words: Cognition disorders; Apathy; Evaluation studies
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".