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 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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".