Validation of the French version of the McLean screening instrument for borderline personality disorder (MSI-BPD) in an adolescent sample
Bibliographic record
Abstract
BACKGROUND: The study examines the psychometric properties of the French version of the McLean Screening Instrument for Borderline Personality Disorder (MSI-BPD) created by M. Zanarini to screen borderline personality disorder in clinical and non-clinical populations. METHOD: In this multicentric longitudinal study from the European Network on Borderline Personality Disorder, a sample of 84 adolescent patients from five psychiatric centres and 85 matched controls without psychiatric comorbidity completed the MSI-BPD, French version, and were interviewed with the Structured Interview for DSM-IV Personality (SIDP-IV), in order to assess the presence or absence of borderline personality disorder. RESULTS: The MSI-BPD showed excellent internal consistency (α = 0.87 [0.84;0.90]). Compared to the semi-structured reference interview (SIDP-IV), the MSI-BPD showed substantial congruent validity (AUC = 0.93, CI 95%: 0.90-0.97). The optimal cut-off point in the present study was 5 or more, as it had relatively high sensitivity (0.87) and specificity (0.85). In our sample, the cut-off point (7 or more) proposed by the original developers of the MSI-BPD showed high specificity (0.95) but low sensitivity (0.63). CONCLUSIONS: The French version of the MSI-BPD is now available, and its psychometric properties are satisfactory. The French version of the MSI-PBD can be used as a screening tool for borderline personality disorder, for clinical purposes or in research studies.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".