The screen for cognitive impairment in psychiatry in patients with borderline personality disorder
Bibliographic record
Abstract
Cognitive deficits are common in borderline personality disorder (BPD) and appear to be associated with psychopathology, functioning and outcome. The availability of a cognitive screening instrument could be of use in clinical settings in order to assess neurocognition in BPD patients. The Screen for Cognitive Impairment for Psychiatry (SCIP) proved to be reliable in different psychiatric populations, but it has not yet been validated in personality disorders. The purpose of this study is therefore to evaluate its psychometric properties in a sample of 58 BPD patients. The SCIP was validated against the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and the Trail Making Test A and B (TMT A and B). The receiver operator curve analysis displayed an acceptable convergent validity (total score AUC: 0.78, 95% CI: 0.70-0.86; Se: 75%, Sp: 72%). A cut-off total score of 80 identified 81% of patients as cognitively impaired. The exploratory factor analysis displayed a one-factor solution explaining 55.8% of the total variance. The SCIP displayed adequate psychometric properties in BPD and could be integrated in the routine clinical assessment to provide a preliminary evaluation of cognitive features for BPD.
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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) | 0.000 | 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".