Clinical usefulness of the screen for cognitive impairment in psychiatry (SCIP-S) scale in patients with type I bipolar disorder
Bibliographic record
Abstract
BACKGROUND: The relevance of persistent cognitive deficits to the pathogenesis and prognosis of bipolar disorders (BD) is understudied, and its translation into clinical practice has been limited by the absence of brief methods assessing cognitive status in Psychiatry. This investigation assessed the psychometric properties of the Spanish version of the Screen for Cognitive Impairment in Psychiatry (SCIP-S) for the detection of cognitive impairment in BD. METHODS: After short training, psychiatrists at 40 outpatient clinics administered the SCIP three times over two weeks to a total of 76 consecutive type I BD admissions. Experienced psychologists also administered a comprehensive battery of standard neuropsychological instruments to clinical sample and 45 healthy control subjects. RESULTS: Feasibility was supported by a brief administration time (approximately 15 minutes) and minimal scoring errors. The reliability of the SCIP was confirmed by good equivalence of forms, acceptable stability (ICC range 0.59 to 0.87) and adequate internal consistency (Chronbach's alpha of 0.74). Construct validity was granted by extraction of a single factor (accounting 52% of the variance), acceptable correlations with conventional neuropsychological instruments, and a clear differentiation between bipolar I and normal samples. Efficiency was also provided by the adequate sensitivity and specificity. LIMITATIONS: The sample size is not very large. The SCIP and the neurocognitive battery do not cover all potentially relevant cognitive domains. Also, sensitivity to change remains unexplored. CONCLUSION: With minimal training, physicians obtained a reliable and valid estimate of cognitive impairment in approximately 15 minutes from an application of the SCIP to type I BD patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".