The screen for cognitive impairment in psychiatry: diagnostic-specific standardization in psychiatric ill patients
Bibliographic record
Abstract
BACKGROUND: The Screen for Cognitive Impairment in Psychiatry (SCIP) is a simple and easy to administer scale developed for screening cognitive deficits. This study presents the diagnostic-specific standardization data for this scale in a sample of schizophrenia and bipolar I disorder patients. METHODS: Patients between 18 and 55 years who are in a stable phase of the disease, diagnosed with schizophrenia, schizoaffective disorder, schizophreniform disorder, or bipolar I disorder were enrolled in this study. RESULTS: The SCIP-S was administered to 514 patients (57.9% male), divided into two age groups (18-39 and 40-55 years) and two educational level groups (less than and secondary or higher education). The performance of the patients on the SCIP-S is described and the transformed scores for each SCIP-S subtest, as well as the total score on the instrument, are presented as a percentile, z-score, T-scores, and IQ quotient. CONCLUSIONS: We present the first jointly developed benchmarks for a cognitive screening test exploring functional psychosis (schizophrenia and bipolar disorder), which provide increased information about patient's cognitive abilities. Having guidelines for interpreting SCIP-S scores represents a step forward in the clinical utility of this instrument and adds valuable information for its use.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".