THINC-Integrated Tool (THINC-it): A Brief Measurement of Changes in Cognitive Functioning and Its Correlation with the Life Quality of Patients with Schizophrenia and Related Disorders—A Pilot Study
Bibliographic record
Abstract
Background: This pilot study aimed to assess patients’ cognitive functioning with the Polish version of the THINC-it tool and to analyze its association with self-reported quality of life (QOL). Methods: Twenty-one patients (mean age: 37.8 ± 10.4) were assessed at baseline and after six weeks of a standard therapeutic outpatient program. Participants completed the World Health Organization QOL Questionnaire (WHOQOL-BREF) and the THINC-it tool at both visits. The tool consists of tasks evaluating working memory (SYMBOL CHECK), attention (SPOTTER), executive functions (TRIALS), and cognitive skills (CODEBREAKER). Results: During the second visit, patients showed significant improvements in mean latency of correct responses of SPOTTER: p = 0.021, Cohen’s d = 0.38 and in the Physical health domain: p = 0.007, Cohen’s d = 0.37. The number of correct responses for CODEBREAKER was positively associated with the Physical health domain at visit 1 (r = 0.53, p = 0.014) and visit 2 (r = 0.42, p = 0.058). The number of correct responses at SYMBOL CHECK was positively related to QOL in the Environment domain only at visit 2 (r = 0.45, p = 0.042). Conclusions: These results suggest the THINC-it tool has utility as a cognitive measure in adults with schizophrenia in both clinical and research settings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".