PSYCHOMETRIC properties of the Chinese version of the THINC-it tool for cognitive symptoms in patients with major depressive disorder
Bibliographic record
Abstract
BACKGROUND: To validate the reliability and validity of the Chinese version of the THINC-it tool in adults with major depressive disorder (MDD). METHODS: Subjects aged 18 to 65 years (n=117) with MDD were evaluated and compared to age- and sex-matched healthy controls (n=124). Subjects completed the THINC-it, four criteria-related objective cognitive subtests, and the paper version of Perceived Deficits Questionnaire for Depression-5-item (PDQ-5-D). RESULTS: There were significant differences in Spotter [Mean difference (MD) Standard errors (SE)=-0.40 (0.17), P=0.018; 95% Confidence intervals (CI) (-0.73 to -0.07)]; Codebreaker [MD (SE)=-0.39 (0.14), P=0.006; 95% CI(-0.67 to -0.11)]; and the overall performance of four objective tests (including variants of IDN, OBK, DSST, and TMT-B) [MD (SE)=-0.30 (0.12), P=0.013; 95% CI (-0.53 to -0.06]) between the two groups. In the HC group, PDQ-5-D retest reliability was good (ICC = 0.841), and the test-retest reliability of the objective cognitive test was relatively low (ICC ranging from 0.123 to 0.545). In the MDD group: Cronbach's α of PDQ-5-D=0.704; all the THINC-it subtests had good concurrent validity (r ranging from 0.343 to 0.835, all P<0.01). LIMITATION: The test-retest sample size was relatively small, the educational level and IQ of the control and MDD groups were not completely matched. CONCLUSION: The Chinese version of the THINC-it tool exhibits good reliability and validity in adults with MDD. There is a need to incorporate cognitive assessment of adults with MDD broadly. The THINC-it tool is the first tool validated to assess cognition of MDD in a Chinese population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".