Development and validation of the Music Cognitive Test: A music-based cognitive screening test
Bibliographic record
Abstract
Specific tools to measure the cognitive benefits of music therapy and music-rehabilitation training available to music therapists are few and empirically weak: they are mostly psychometrically unrefined or based on unclear tasks and scoring protocols; they do not take into consideration distinct cognitive functions or are based on exclusively observational protocols. To overcome these limitations, we developed a 15-min cognitive screening tool suitable for music therapists, Music Cognitive Test (MCT), which assesses cognitive abilities stimulated by music-making activities (e.g., attentional, verbal, and executive functions, short- and long-term memory) by including music-based items. MCT was validated with 335 participants (aged 18–100 years old) presenting a range of cognitive levels, from healthy cognition to severe impairment. MCT correlated strongly and positively with well-known tests: Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), and Severe Mini-Mental State Examination (SMMSE). MCT also displayed excellent sensitivity in identifying impaired individuals according to both MMSE and MoCA diagnostic criteria (99.4% and 93.0%, respectively), and excellent specificity in identifying healthy participants based on MMSE (93.5%) and MoCA (97.8%) criteria. Overall, results highlight the reliability of this novel brief music-focused cognitive screening test, to enable music therapists to independently and consistently monitor the effectiveness of their intervention on cognitive functions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".