Mapping of validated apathy scales onto the apathy diagnostic criteria for neurocognitive disorders
Bibliographic record
Abstract
BACKGROUND: Diagnostic criteria for apathy in neurocognitive disorders (DCA-NCD) have recently been updated. OBJECTIVES: We investigated whether validated scales measuring apathy severity capture the three dimensions of the DCA-NCD (diminished initiative, diminished interest, diminished emotional expression). MEASUREMENTS: Degree of mapping ("not at all", "weakly", or "strongly") between items on two commonly used apathy scales, the Neuropsychiatric Inventory-Clinician (NPI-C) apathy and Apathy Evaluation Scale (AES), with the DCA-NCD overall and its 3 dimensions was evaluated by survey. DESIGN: Survey participants, either experts (n = 12, DCA-NCD authors) or scientific community members (n = 19), rated mapping for each item and mean scores were calculated. Interrater reliability between expert and scientific community members was assessed using Cohen's kappa. RESULTS: According to experts, 9 of 11 (81.8%) NPI-C apathy items and 6 of 18 (33.3%) AES items mapped strongly onto the DCA-NCD overall. For the scientific community group, 10 of 11 (90.9%) NPI-C apathy items and 7 of 18 (38.8%) AES items mapped strongly onto the DCA-NCD overall. The overall mean mapping scores were higher for the NPI-C apathy compared to the AES for both expert (t (11) = 3.13, p = .01) and scientific community (t (17) = 3.77, p = .002) groups. There was moderate agreement between the two groups on overall mapping for the NPI-C apathy (kappa= 0.74 (0.57, 1.00)) and AES (kappa= 0.63 (0.35, 1.00)). CONCLUSIONS: More NPI-C apathy than AES items mapped strongly and uniquely onto the DCA-NCD and its dimensions. The NPI-C apathy may better capture the DCA-NCD and its dimensions compared with the AES.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".