The THINC-Integrated Tool (THINC-it) Screening Assessment for Cognitive Dysfunction
Bibliographic record
Abstract
OBJECTIVE: To validate the THINC-integrated tool (THINC-it)-a freely available, patient-administered, computerized screening tool integrating subjective and objective measures of cognitive function in adults with major depressive disorder (MDD). METHODS: Subjects aged 18 to 65 years (n = 100) with recurrent MDD experiencing a major depressive episode of at least moderate severity were evaluated and compared to age-, sex-, and education-matched healthy controls (n = 100). Between January and June 2016, subjects completed the THINC-it, which includes variants of the Choice Reaction Time Identification Task (IDN), One-Back Test, Digit Symbol Substitution Test, Trail Making Test-Part B, and the Perceived Deficits Questionnaire for Depression-5-item (PDQ-5-D). RESULTS: The THINC-it required approximately 10 to 15 minutes for administration and was capable of detecting cognitive deficits in adults with MDD. A total of 44.4% of adults with MDD exhibited cognitive performance at ≥ 1.0 SD below that of healthy controls on standardized mean scores of the THINC-it. Concurrent validity of the overall tool, based on a calculated composite score, was acceptable (r = 0.539, P < .001). Concurrent validity of the component tests ranged from -0.083 (IDN) to 0.929 (PDQ-5-D). Qualitative survey results indicated that there was a high level of satisfaction and perceived value in administering the THINC-it regarding its impact on the appropriateness and quality of care being received. CONCLUSIONS: The THINC-it is a valid and sensitive tool for detecting cognitive dysfunction in adults with MDD that is free, easy to use, and rapidly administered. The THINC-it should be incorporated into the assessment and measurement of all patients with MDD, particularly among those with enduring functional impairment. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02508493.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".