Psychometric properties of the MATRICS Consensus Cognitive Battery in young patients with bipolar disorder
Bibliographic record
Abstract
ObjectiveTo investigate the psychometric features of MATRICS Consensus Cognitive Battery （MCCB） in adolescents with bipolar disorder， so as to evaluate its appropriateness for the measurement of cognitive deficits in adolescents with bipolar disorder.MethodsAdolescents with bipolar disorder （n=38）， adolescents with major depressive episode （n=40） and healthy controls （n=41） matched on age， sex and educational background were enrolled. Adolescents with bipolar disorder were assessed using Montreal Cognitive Assessment Scale （MoCA） and MCCB at baseline and 2 weeks later， while the rest were only assessed using MCCB at baseline. Thereafter， the psychometric features of MCCB such as internal consistency， test-retest reliability and criterion-related validity， discriminant validity and structural validity were evaluated using Cronbach's α coefficient， Pearson correlation analysis， analysis of covariance and confirmatory factor analysis.Results①The Cronbach's α coefficient of MCCB in adolescents with bipolar disorder was 0.784 at baseline and 0.773 at two weeks later， respectively. ②Among adolescents with bipolar disorder， the test-retest reliability over a two-week interval of each dimension in MCCB ranged from 0.630 to 0.812 （P<0.01）. ③ The criterion-related validity denoted that the score of short-term memory domain in MoCA was positively correlated with the speed of processing， verbal learning and working memory in MCCB （r=0.487， 0.522， P<0.05 or 0.01）. ④ Discriminant validity analysis implied that the scores of the processing speed， attention/vigilance， working memory， verbal learning and memory， visual learning and memory， reasoning and problem solving in MCCB yielded statistical differences among adolescents with bipolar disorder， adolescents with major depressive episode and healthy controls （F=3.790~7.243， P<0.01）. ⑤ Exploratory factor analysis showed that cumulative total variance contribution rate of MCCB amounted to 71.65% of four factors， and the confirmatory factor analysis indicated that the ideal 7-factor model had poor structural validity.ConclusionMCCB has good internal consistency， retest reliability and acceptable validity in adolescents with bipolar disorder.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".