The screen for cognitive impairment in psychiatry (SCIP) is associated with disease severity and cognitive complaints in major depression
Bibliographic record
Abstract
Objective: To assess the relationship between the Screen for Cognitive Impairment in Psychiatry (SCIP) score and illness severity, subjective cognition and functioning in a cohort of major depressive disorder (MDD) patients.Methods: Patients (n = 40) diagnosed with MDD (DSM-IV-TR) completed the SCIP, a brief neuropsychological test, and a battery of self-administered questionnaires evaluating functioning (GAF, SDS, WHODAS 2.0, EDEC, PDQ-D5). Disease severity was evaluated with the Hamilton Depression Rating Scale (HDRS) and the Clinical Global Impression (CGI).Results: Age and sex were associated with performance in the SCIP. The SCIP-Global index score was associated with disease severity (r = −0.316, p < .05), the SDS, a patient self-assessment of daily functioning (r = −0.368, p < .05), and the EDEC subscales of patient-reported cognitive deficits (r = −0.388, p < .05) and their functional impacts (r = −0.335, p < .05). Multivariate analysis adjusted for age and sex confirmed these tests are independent predictors of performance in the SCIP (CGI-S, F[3,34] = 4.478, p = .009; SDS, F[3,34] = 3.365, p = .030; EDEC-perceived cognitive deficits, F[3,34] = 5.216, p = .005; EDEC-perceived impacts of functional impairment, F[3,34] = 5.154, p = .005).Conclusions: This study confirms that the SCIP can be used during routine clinical evaluation of MDD, and that cognitive deficits objectively assessed in the SCIP are associated with disease severity and self-reported cognitive dysfunction and impairment in daily life.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".