The screen for cognitive impairment in psychiatry (SCIP) as a routinely applied screening tool: pathology of acute psychiatric inpatients and cluster analysis
Bibliographic record
Abstract
BACKGROUND: Cognitive dysfunction has been reported in acute psychiatric patients for a long time. The detection of cognitive deficits is crucial both for clinical treatment and for predicting the psychosocial functional level in the further course of the disease. The SCIP is a well-evaluated screening instrument for the examination of cognitive performance in psychiatric patients. We recently integrated the SCIP into our routine admission and discharge assessments on two inpatient wards, and we examined the cognitive profiles of patients with psychotic and affective disorders over the course of their admission. METHODS: Shortly after admission, and prior to discharge, patients were routinely referred for examination with the SCIP. A total of 529 assessments were completed on admission, and 227 returned for SCIP at the time of discharge. After standardization of the test results against a normative sample, we examined the normalized test values in terms of percentages of pathological cognitive performance based on the total SCIP score, and each of the SCIP subscale scores. We conducted cluster analysis to identify cognitive subgroups within the clinical sample. RESULTS: More than 70% of the SCIP results on admission were pathological. At discharge, improvements were observed, especially on tests with attention and speed components. Cluster analysis identified two groups. The cluster with chronic patients showed poorer results at admission, but greater improvement and reached the level of the others at discharge. CONCLUSIONS: The SCIP appears to have value in routine diagnostic assessments, and in the quantification of improvements in cognitive performance during an inpatient stay. The greatest benefit was observed in chronically ill patients with many previous stays. TRIAL REGISTRATION: DRKS00019825 (retrospectively registered on 03.12.2019).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".